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Research Article

The characteristics and extent of food industry involvement in peer-reviewed research articles from 10 leading nutrition-related journals in 2018

Roles Conceptualization, Formal analysis, Investigation, Methodology, Project administration, Writing – original draft, Writing – review & editing

* E-mail: [email protected]

Affiliation Global Obesity Centre (GLOBE), Institute for Health Transformation, School of Health and Social Development, Faculty of Health, Deakin University, Geelong, Victoria, Australia

Roles Formal analysis, Investigation, Writing – review & editing

Roles Conceptualization, Formal analysis, Writing – review & editing

Affiliation School of Public Health, University of Sao Paulo, Sao Paulo, Brazil

Roles Formal analysis, Writing – review & editing

Roles Conceptualization, Investigation, Writing – review & editing

• Gary Sacks, 
• Devorah Riesenberg, 
• Melissa Mialon, 
• Sarah Dean, 
• Adrian J. Cameron

• Published: December 16, 2020
• https://doi.org/10.1371/journal.pone.0243144
• Peer Review
• Reader Comments

Introduction

There is emerging evidence that food industry involvement in nutrition research may bias research findings and/or research agendas. However, the extent of food industry involvement in nutrition research has not been systematically explored. This study aimed to identify the extent of food industry involvement in peer-reviewed articles from a sample of leading nutrition-related journals, and to examine the extent to which findings from research involving the food industry support industry interests.

All original research articles published in 2018 in the top 10 most-cited nutrition- and dietetics-related journals were analysed. We evaluated the proportion of articles that disclosed involvement from the food industry, including through author affiliations, funding sources, declarations of interest or other acknowledgments. Principal research findings from articles with food industry involvement, and a random sample of articles without food industry involvement, were categorised according to the extent to which they supported relevant food industry interests.

196/1,461 (13.4%) articles reported food industry involvement. The extent of food industry involvement varied by journal, with The Journal of Nutrition (28.3%) having the highest and Paediatric Obesity (3.8%) having the lowest proportion of industry involvement. Processed food manufacturers were involved in the most articles (77/196, 39.3%). Of articles with food industry involvement, 55.6% reported findings favourable to relevant food industry interests, compared to 9.7% of articles without food industry involvement.

Food industry involvement in peer-reviewed research in leading nutrition-related journals is commonplace. In line with previous literature, this study has shown that a greater proportion of peer-reviewed studies involving the food industry have results that favour relevant food industry interests than peer-reviewed studies without food industry involvement. Given the potential competing interests of the food industry, it is important to explore mechanisms that can safeguard the integrity and public relevance of nutrition research.

Citation: Sacks G, Riesenberg D, Mialon M, Dean S, Cameron AJ (2020) The characteristics and extent of food industry involvement in peer-reviewed research articles from 10 leading nutrition-related journals in 2018. PLoS ONE 15(12): e0243144. https://doi.org/10.1371/journal.pone.0243144

Editor: Quinn Grundy, University of Toronto, CANADA

Received: June 15, 2020; Accepted: November 16, 2020; Published: December 16, 2020

Copyright: © 2020 Sacks et al. This is an open access article distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Data Availability: All relevant data are within the paper and its Supporting Information files.

Funding: GS and AJC were supported by Heart Foundation Future Leader Fellowships (102035 and 36357, respectively) from the National Heart Foundation of Australia ( https://www.heartfoundation.org.au/ ). GS and AJC are researchers within a National Health and Medical Research Council (NHMRC) ( https://www.nhmrc.gov.au/ ) Centre of Research Excellence in Food Retail Environments for Health (RE-FRESH) (APP1152968) (Australia). GS is also a researcher within a NHMRC Centre for Research Excellence entitled Reducing Salt Intake Using Food Policy Interventions (APP1117300). The authors are solely responsible for the opinions, hypotheses and conclusions or recommendations expressed in this publication, and they do not necessarily reflect their funders’ vision. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

Competing interests: GS and AJC are academic partners on a publicly funded healthy supermarket intervention trial that includes Australian local government and supermarket retail (IGA) collaborators. GS has been involved in studies to benchmark the policies and commitments of food companies related to obesity prevention and nutrition in Australia, New Zealand, Canada, Malaysia and Europe. The authors have not received funding from any organization in the food industry. The authors have no other potential competing interests to declare. The competing interests of the authors do not alter our adherence to PLOS ONE policies on sharing data and materials.

Dietary risk factors are associated with more deaths and disability worldwide than any other modifiable factor [ 1 ]. A key driver of poor diets globally has been a nutrition transition characterised by increased consumption of ultra-processed packaged foods [ 2 – 4 ]. These foods are manufactured, marketed and sold by a diverse selection of companies and organisations, collectively referred to as the ‘food industry’ [ 5 ]. Importantly, global food systems are now dominated by a relatively small number of large transnational food companies [ 2 , 6 ]. The continued generation of profit by these large food companies typically relies on aggressive marketing of their products and brands, as well as political strategies to create regulatory environments that facilitate their market power [ 7 ].

Nutrition research is fundamental to efforts to promote healthy eating behaviours and health. However, there is concern regarding how the involvement of the food industry in nutrition research affects the nature of studies conducted, the nutrition research agenda and the findings of individual studies [ 8 – 10 ]. The interests of many commercial food industry actors are misaligned with clinical and public health objectives as the legal mandate of corporations is to return profit for their shareholders, without explicit consideration of broader social impact [ 10 , 11 ]. In recognition of the inherent risks and to preserve the scientific credibility of nutrition-related research, food industry involvement in research is increasingly scrutinized [ 12 , 13 ].

Food industry involvement in research can take many forms. These forms of involvement include, amongst others, the provision of funding and the involvement of food company employees as part of research teams. There are many reasons why food companies might be involved in nutrition-related research. These reasons may include unobjectionable motives such as a willingness to develop new knowledge, assist in research translation and contribute expertise and resources [ 14 ]. However, from a public health perspective, several concerns have been identified regarding food industry involvement in research. These include: 1) the creation of increased marketing opportunities for industry products, many of which are harmful to population health [ 15 ]; 2) the establishment and nurturing of relationships between the food industry and nutrition researchers that serves to increase perceived industry credibility, reduce industry criticism, and encourage increased dependency on the food industry [ 16 , 17 ]; 3) industry influence over research agendas to preferentially focus on topics likely to benefit industry interests, rather than topics of public health importance [ 18 ]; 4) industry influence on the methods, conclusions and impact of research in ways that are likely to favour industry interests over and above other factors [ 9 , 19 – 21 ]; and 5) use of research for political purposes [ 22 , 23 ]. An increased dependence on food industry funding by academics has been documented [ 9 , 12 , 16 , 24 ], with food industry funding sometimes acknowledged as a strategically important funding source for the university sector [ 25 ].

Previous research has investigated the impact of food industry sponsorship on the findings of published research. Several studies have found that papers sponsored by the food industry typically favour industry interests [ 9 , 21 , 26 ], although a recent meta-analysis found that the quantitative difference in conclusions between food industry-sponsored and non–industry-sponsored nutrition studies was not significant [ 8 ]. To date, no study has comprehensively examined the extent and nature of food industry involvement in peer-reviewed research. Better information regarding the extent of food industry involvement, characteristics (e.g., industry sector, company size) of food industry actors that are involved in nutrition-related research, and the ways in which they are involved (e.g., study authorship, different types of funding provided) would assist efforts to assess and manage the potential impact and implications of food industry involvement in research.

This study aimed to contribute to a growing body of empirical evidence related to food industry involvement in peer-reviewed published research by systematically identifying the extent of food industry involvement in research articles from a large sample of leading nutrition-related journals. In addition, this study examined the extent to which research findings support food industry interests for both articles with declared food industry involvement, and those with no declared food industry involvement.

The study examined articles published in 2018 in the top 10 nutrition and dietetics journals as defined by the SCImago Journal ranking (SJR) as at June 2019. The SJR is a measure of a journal’s impact, and expresses the average number of weighted citations received in a selected year by the documents published in the journal in the three previous years [ 27 ]. The selected journals included (in alphabetical order): Advances in Nutrition , Clinical Nutrition , International Journal of Behavioural Nutrition and Physical Activity , International Journal of Obesity , Nutrition Research Reviews , Nutrition Reviews , Obesity , Paediatric Obesity , The American Journal of Clinical Nutrition and The Journal of Nutrition .

Details of all articles (n = 1,732) published in the selected journals in 2018 were extracted from Medline, CINAHL, Global Health or PubMed. Article types included in the study were original research articles, reviews, short/brief reports and short communications. Article types excluded were errata/corrections, editorials, perspectives, letters to the editor and other related article types. We also examined the disclosed conflicts of interest of the editorial board of each of the selected journals (based on information provided on the website of each journal), links of the selected journals and their editors to the food industry (based on biographical information provided on the journal website and/or on the website of each editor’s primary affiliation), as well as each journal’s requirements for authors to disclosure conflicts of interest and any other related policies (based on information provided on the website of each journal).

Food industry involvement

Each included article was examined independently by two of the authors (DR and GS) to determine whether there was food industry involvement in the paper. For the purposes of this study, the “food industry” was broadly defined to include all organisations involved in food and non-alcoholic beverage production, distribution, marketing and retail, as well as relevant industry groups and trade associations [ 28 ]. We included manufacturers of dietary supplements and breast-milk substitutes in this definition. In recognition of the known industry tactic of establishing ‘front groups’ (defined as an organisation that purports to represent one agenda while in reality it serves some other party or interest whose sponsorship is hidden or rarely mentioned) [ 29 ], our definition of “food industry” also included organisations that received the majority of their funding from the food industry.

Food industry involvement was determined based on examination of author affiliations, declared funding sources, declarations of interests, and acknowledgements within each article. All organisations identified through these sections of each article were assessed to determine whether they could be classified as part of the food industry. All universities were considered as not part of the food industry. Organisations known by the authors to be part of the food industry as well as those on an established list of known food industry front groups were classified as such [ 30 ]. Searches of the primary websites of all other organisations were conducted to determine the nature of their operations and their funding sources, where relevant, in order to determine if they could be considered as part of the food industry [ 31 ].

Food industry actors identified through the study were classified into one of nine different sectors: 1) dairy; 2) dietary supplement manufacturing; 3) food chemical suppliers and food technology companies; 4) food retail; 5) meat and livestock; 6) non-alcoholic beverage manufacturing; 7) primary production (non-dairy, non-meat); 8) processed food manufacturing; and 9) other food industry organisations (see S1 Table for definitions of what was included in each sector). Categorisations were based on an assessment of the primary areas of activity of the actor, based on the knowledge of the authors and information provided on the website of the actor. In addition, we classified food industry actors into three categories based on the size and nature of their operations. These included large corporations (with annal global revenue > USD1 billion), trade/industry associations, and small corporations/other entities (annual global revenue < USD1 billion). This classification was based on information obtained from the Euromonitor Passport database [ 32 ], supplemented by internet searches of the name of the food industry actor where necessary. All categorisation of food industry actors was performed independently by two of the authors (DR and SD), with any discrepancies discussed and resolved with a third author (GS).

Based on the information extracted, papers were categorised as having food industry involvement if: 1) any of the authors self-affiliated as an employee, member or representative of the food industry; 2) the authors declared funding from the food industry, including direct funding for the study, donation of products to be used for the study, or funding received for other activities (e.g., conference attendance) not directly related to the study; or 3) other stated food industry involvement (e.g., through conflicts noted in the acknowledgments sections or other involvement that did not fit within the other categories). Where an individual article included multiple forms of industry involvement, each form of involvement was noted.

Classification of principal findings

The ‘principal findings’ of all articles that had involvement with the food industry were classified according to whether the findings were: 1) favourable to the interests of the food industry actor; 2) unfavourable to the interests of the food industry actor; 3) mixed; 4) neutral; or 5) not applicable to the food industry actor/s involved (see Table 1 for definition of each classification). The principal findings were operationalised as the results that were reported in the ‘results’ section of the abstract of the paper. If the relevant section of the abstract contained insufficient information to deduce the nature of the principal findings, the ‘results’ and ‘discussion’ sections of the paper were also examined to understand the nature of the principal reported findings. This approach was based on methods previously used for similar types of analyses [ 8 , 33 ].

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https://doi.org/10.1371/journal.pone.0243144.t001

For each of the ten journals, a sample of randomly selected original research articles that did not report food industry involvement was also selected. The process for selection of these articles was that, first, the number of articles with food industry involvement for each journal was calculated. Then, the matching number of articles from each journal, but without food industry involvement, was selected randomly from the list of included articles using the RAND function in Excel. Accordingly, an equal number of articles with and without industry involvement in each journal was selected for analysis. The principal findings of all selected articles without food industry involvement were examined and classified in the same way as the principal findings of the articles with food industry involvement. As there was no specific industry actor involved in these articles, a broad interpretation of food industry interests was taken when assessing the extent to which articles favoured food industry interests. For example, a favourable finding for any food product or nutrient was considered favourable to the food industry, whereas a negative finding for any food product or nutrient was considered unfavourable. The primary topic area of each of the articles was noted, including the particular foods, food components or nutrients (as relevant).

Assessments of principal findings were conducted independently by two of the authors (DR and SD), with any discrepancies discussed and resolved with a third author (GS). Results were analysed by type of food industry involvement and by journal. For the purposes of this analysis of ‘type of food industry involvement’, author affiliations with the food industry and direct funding for the study from the food industry were grouped together (as they were considered more direct involvement) and compared to other types of food industry funding (that were considered less direct involvement).

Statistical analysis

All articles with food industry involvement were identified from each of the ten included journals, with the frequency and percentage in each category of favourability calculated. For the randomly selected matched sample of research articles with no food industry involvement, we calculated 95% confidence intervals (CIs) for the proportion of articles in each category of favourability (e.g., favourable or unfavourable to food industry interests) using Stata 15.0 (StataCorp, College Station, TX, USA).

Of the 1,732 articles published in the selected journals, 1,461 peer-reviewed research articles met our inclusion criteria (n = 271 excluded) ( Fig 1 ) . Amongst these, 196/1,461 (13.4%) were classified as having food industry involvement ( Table 2 ). Refer to S2 Table for details of food industry actors identified.

https://doi.org/10.1371/journal.pone.0243144.g001

https://doi.org/10.1371/journal.pone.0243144.t002

The most common form of involvement was the provision of direct funding for the study (n = 120/196, 61.2%). Other involvement (including acknowledgments and information listed in the conflict of interests section and not related to other categories) represented the second most common form of involvement (82/196, 41.8%) followed by industry funding received for other research not directly related to the study (70/196, 35.7%) and authorship (59/196, 30.1%) ( Table 2 ).

Food industry involvement was noted across all 10 journals included in the sample. The Journal of Nutrition (28.3%), Nutrition Reviews (24.5%), and The American Journal of Clinical Nutrition (16.7%) published the highest proportion of articles with food industry-involvement. Paediatric Obesity (3.8%), International Journal of Behavioural Nutrition and Physical Activity (4.0%), and International Journal of Obesity (4.9%) published the lowest proportion of articles with food industry involvement ( Table 2 ). Each journal had similar policies in place that required authors to disclose conflicts of interest. Four journals ( Advances in Nutrition , The Journal of Nutrition , Obesity , Paediatric Obesity ) included statements regarding conflicts of interest of their editorial board on the journal website. Editors from six journals ( The American Journal of Clinical Nutrition , Advances in Nutrition , International Journal of Obesity , Nutrition Reviews , The Journal of Nutrition , Obesity ) were identified as having involvement with the food industry (see S3 Table ). No other relevant policies regarding studies with food industry involvement were identified by any journal.

A diverse range of sectors of the food industry were involved in the research assessed ( Table 3 ). The sectors most often represented were processed food manufacturing (39.3%), dietary supplement manufacturing (28.6%) and dairy (27.0%). Food retailers (including supermarkets) were involved in the fewest papers (2.6%). Of the 161 food industry actors identified as involved in research articles, the highest proportion (41.6%) were classified as trade/industry associations, 35.4% were classified as small corporations/other entities, and 23.0% were classified as large corporations ( S4 Table ). However, these large corporations were the most frequently involved (47.8% of identified instances of food industry involvement), followed by trade/industry associations (36.4% of identified instances of food industry involvement) and small corporations/other entities (15.8% of identified instances of food industry involvement) ( S4 Table ). Refer to S5 Table for further information on the industry actors identified as being involved in more than 1% of articles.

https://doi.org/10.1371/journal.pone.0243144.t003

The majority of papers with food industry involvement reported findings that were considered favourable to the food industry (n = 109, 55.6%) ( Table 4 ). The proportion of articles with findings considered favourable to the food industry was even higher (66.2%) where study authors reported either affiliations related to the food industry or direct funding for the study from the food industry ( Table 4 ). In contrast, of the 196 randomly selected articles with no identified food industry involvement, 19 (9.7%, 95% CI: 7.0–12.4) reported findings classified as favourable to the food industry. The vast majority (n = 15/19, 78.9%) of these articles related to particular nutrients and/or food components (e.g., protein, vitamins), with the remaining four articles (21.1%) relating to foods and food products (e.g., coffee, green tea) ( S6 Table ).

https://doi.org/10.1371/journal.pone.0243144.t004

Only a small proportion (n = 13, 6.6%) of papers with food industry involvement reported results that were unfavourable to the food industry ( Table 4 ). The percentage of articles with findings unfavourable to the food industry or mixed findings were similar for those articles with and without food industry involvement ( Table 4 ). 117 (59.7%, 95% CI: 54.5–65.7) articles with no food industry involvement had findings considered not applicable to the food industry, compared to 50 (25.5%) of the articles with food industry involvement. Similar patterns were observed across each journal ( S7 Table ).

This study found that 13.4% of peer-reviewed research articles in the top 10 most-cited nutrition- and dietetics-related journals from 2018 reported food industry involvement. Food industry involvement spanned a number of industry sectors, with processed food manufacturing, dietary supplement manufacturing and dairy most often represented. The vast majority of industry involvement was from large corporations and trade/industry associations, rather than smaller corporations. The proportion of articles with findings considered favourable to the food industry was substantially higher among those articles with food industry involvement (55.6%) compared to a random sample of those without (9.7%), with the difference even more marked where industry involvement in studies was more direct (author affiliations or direct funding for the study). The percentage of articles considered unfavourable to the interests of the food industry was similar among the articles with food industry involvement and the random sample of those articles without.

Considerable variation in the percentage of articles with industry involvement was observed between journals. The Journal of Nutrition and Nutrition Reviews published the highest proportion of articles with industry involvement. Both of these journals have declared connections to the food industry. Several members of the board of The Journal of Nutrition have declared conflicts of interest involving food companies [ 34 ]. The Journal of Nutrition is published by the American Society of Nutrition (ASN), which has formal partnerships with multiple food companies [ 35 ] and has been criticised for supporting food industry objectives over public health interests [ 24 ]. Other journals included in the sample ( The American Journal of Clinical Nutrition and Advances in Nutrition ) are also published by ASN, and had lower proportions of articles with food industry involvement compared to The Journal of Nutrition . Nutrition Reviews is published by the International Life Science Institute (ILSI), who were founded and are solely funded by large food industry companies including Mars, Nestlé, Coca-Cola and PepsiCo with the majority of their members’ interests opposing public health policy and objectives [ 36 , 37 ]. Future research should explore the extent to which a journal’s connections to the food industry influence their publication priorities and editorial processes.

The findings in this study support existing evidence that research with food industry involvement is generally favourable to the interests of the food industry [ 8 , 11 , 15 , 18 , 21 , 24 , 26 , 38 , 39 ]. In particular, this study adds to the growing empirical evidence that food industry involvement in nutrition research likely influences research agendas to focus disproportionately on topics of importance to the industry, potentially at the expense of topics of greater public health importance [ 8 , 18 ]. A recent scoping review by Fabbri and colleagues [ 18 ] demonstrated the impact of industry involvement across a range of diverse sectors (including medicine and nutrition), finding that industry-funded research was more often focused on products, processes or activities that can be commercialised and marketed, rather than non-market based activities. They concluded that “corporate interests can drive research agendas away from questions that are the most relevant for public health” [ 18 ]. In addition, food industry-funded research has been noted as often focusing on a specific nutrient, potentially enabling the funder to market the benefits of particular nutrients [ 24 ]. While it has previously been reported that nutrition research funded by the food industry typically respects scientific standards for conducting and reporting scientific studies [ 17 ], the food industry was itself involved in that assessment, and the issue warrants further detailed exploration.

It has been well documented that a range of industries, including the food industry, seek involvement in research, develop research that is favourable to their interests, and make use of scientific evidence as part of broader efforts to influence public health policy [ 19 , 22 , 29 , 40 – 42 ]. Moreover, there is evidence that major corporations have pushed for policy making systems that provide a route for feeding corporate evidence into policy making [ 42 , 43 ]. There are several examples of topic areas in which research funded by the food industry favours particular products or diverts attention away from a public health issue. For example, with respect to sugar-sweetened beverages (SSBs), a body of research suggests that the involvement of the SSB sector in research has resulted in research that reports favourable findings for the industry [ 11 , 44 ]. In addition, researchers have documented instances where Coca-Cola maintained control over study data and the disclosure of results for research it funded. Some research agreements between the company and their contracted researchers stated that Coca-Cola had the ultimate choice regarding publication of research findings [ 45 ].

Study limitations

To date, this is the first study to systematically examine the extent of involvement of the food industry in peer-reviewed research articles published in the leading nutrition and dietetics journals. Importantly, much peer-reviewed nutrition research is published outside of the selected nutrition and dietetics journals. Moreover, the study was not designed to identify research with food industry involvement that is published in topic areas outside of nutrition and dietetics, outside of peer-reviewed journals, or that is funded or conducted by the industry but remains unpublished. Accordingly, the study represents only a small and selected analysis of the extent of food industry involvement in nutrition research. Future studies should investigate nutrition-related articles from journals with both a nutrition and non-nutrition focus (including, for example, journals in medicine and public health). Ways to automate methods for comprehensively identifying different types of food industry involvement in published studies need to be explored.

The classification of the principal findings of studies as favourable or unfavourable to the interests of the food industry was based on the knowledge of the researchers involved, which may have led to instances of unintended misclassification. Given the magnitude of the differences observed between articles with and without food industry involvement, unintended misclassifications are highly unlikely to have impacted the overall conclusions.

We did not perform any analysis by study design of the included articles or in relation to the appropriateness and rigour of the research methods used in each article. Accordingly, we did not assess the influence of food industry involvement on scientific methods or the way in which they were applied. Aspects of study design and specific mechanisms by which food industry involvement may influence study focus areas and results should be included in future studies.

The analysis relied primarily on the self-disclosure of food industry involvement (through declared conflicts of interests, funding acknowledgments, and author affiliations), with different journals having different disclosure requirements. We did not conduct an analysis of the veracity of each journal’s conflict of interest disclosure requirements, but this warrants further exploration. Importantly, undisclosed food industry involvement cannot be captured using the approach we adopted in this study. There is evidence that the disclosure of conflict of interest is under-reported in research [ 45 , 46 ], indicating that the percentage of articles with food industry involvement may be larger than that observed here. In addition, our identification of food industry organisations involved in the included studies may have been incomplete. While we made use of an established list of food industry front groups as well as online searches of identified organisations to determine the nature of their operations and funding sources, it has previously been noted that financial links to the food industry are often not publicly available [ 30 ].

Finally, we did not conduct a detailed examination of the extent to which the editors of each journal have links to the food industry. Future research should further explore links between journal editors and the food industry and the role of journal editors in assessing conflicts of interest with the food industry.

Implications of the findings

The finding that food industry involvement is commonplace in peer-reviewed research in leading nutrition-related journals has several implications. With increased recognition of food industry bias within research, it is important to consider ways of maximising the integrity of research published in respected peer-reviewed nutrition journals and ensuring that research focused on issues of public health relevance is prioritized. One option could be to limit industry funding of research to a government- or independently-controlled pool of money that supports a research agenda developed independent of industry, with strict processes to ensure freedom from industry influence [ 47 ]. A similar model for pharmaceutical research already operates in Italy [ 48 ], and in relation to the tobacco and alcohol industry in California and Thailand [ 49 ].

Further, it is important that research institutions have strict, regularly updated and transparent guidelines and policies to regulate and report on their engagement with industry, including specifying the level of engagement permitted with different actors. For those institutions with food industry involvement, processes need to be put in place to ensure that the potential influence of the food industry on research agendas and research methods are managed [ 50 ]. Example of guidelines for managing engagement with industry include those from the Charles Perkins Centre at the University of Sydney [ 51 ] and the Global Obesity Centre at Deakin University in Australia [ 52 ].

Journals could also consider adopting detailed policies regarding articles with declared food industry involvement. Such policies could place limits on the number of articles that the journal will accept for review, specific topic areas where food industry involvement is discouraged, or specific sections in journals for studies with industry involvement [ 24 ]. Based on the findings of this study, all articles that include any type of food industry involvement warrant close scrutiny from journals, with a particular focus on more direct types of involvement (e.g., author affiliations and direct funding for a study). Journals should also have clear policies on disclosing editorial conflicts of interest, including any links between editors and the food industry. Moreover, any such conflicts need to be actively managed or eliminated. Further, research that investigates appropriate standards of disclosure and involvement can guide policy and practice in this area.

Food industry involvement in peer-reviewed nutrition research is commonplace, and the results of the majority of studies with food industry involvement favour the interests of the food industry. Given the potential competing interests of the food industry on the one hand, and scientific and population health interests on the other, it is important to explore mechanisms that can safeguard the integrity and public relevance of nutrition research, and ensure they are not undermined by the influence of the food industry.

Supporting information

S1 table. definitions of categories used to classify organisations from the food industry..

https://doi.org/10.1371/journal.pone.0243144.s001

S2 Table. Food industry actors identified as involved in research studies in the top 10 most-cited nutrition- and dietetics-related journals in 2018, by food industry sector and actor classification.

https://doi.org/10.1371/journal.pone.0243144.s002

S3 Table. The top 10 most-cited nutrition- and dietetics-related journals in 2018 and their declared involvement with the food industry.

https://doi.org/10.1371/journal.pone.0243144.s003

S4 Table. Food industry actors identified as being involved in the top 10 most-cited nutrition- and dietetics-related journals in 2018.

https://doi.org/10.1371/journal.pone.0243144.s004

S5 Table. Food industry actors identified as being involved in more than 1% of articles examined in the top 10 most-cited nutrition- and dietetics-related journals in 2018.

https://doi.org/10.1371/journal.pone.0243144.s005

S6 Table. Primary topic area of the random sample of articles without food industry involvement 1 .

https://doi.org/10.1371/journal.pone.0243144.s006

S7 Table. Nature of the findings in articles with and without 1 food industry involvement, by journal.

https://doi.org/10.1371/journal.pone.0243144.s007

Acknowledgments

The authors would like to acknowledge the contribution of Benjamin Sullivan, an Honours student in the School of Health and Social Development at Deakin University in 2015, whose research informed the design of this study.

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• Published: 20 April 2021

Beyond nutrition and physical activity: food industry shaping of the very principles of scientific integrity

• Mélissa Mialon   ORCID: orcid.org/0000-0002-9883-6441 1 ,
• Matthew Ho 2 ,
• Angela Carriedo 3 ,
• Gary Ruskin 4 &
• Eric Crosbie 5 , 2  

Globalization and Health volume  17 , Article number:  37 ( 2021 ) Cite this article

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There is evidence that food industry actors try to shape science on nutrition and physical activity. But they are also involved in influencing the principles of scientific integrity. Our research objective was to study the extent of that involvement, with a case study of ILSI as a key actor in that space. We conducted a qualitative document analysis, triangulating data from an existing scoping review, publicly available information, internal industry documents, and existing freedom of information requests.

Food companies have joined forces through ILSI to shape the development of scientific integrity principles. These activities started in 2007, in direct response to the growing criticism of the food industry’s funding of research. ILSI first built a niche literature on COI in food science and nutrition at the individual and study levels. Because the literature was scarce on that topic, these publications were used and cited in ILSI’s and others’ further work on COI, scientific integrity, and PPP, beyond the fields of nutrition and food science. In the past few years, ILSI started to shape the very principles of scientific integrity then and to propose that government agencies, professional associations, non-for-profits, and others, adopt these principles. In the process, ILSI built a reputation in the scientific integrity space. ILSI’s work on scientific integrity ignores the risks of accepting corporate funding and fails to provide guidelines to protect from these risks.

Conclusions

The activities developed by ILSI on scientific integrity principles are part of a broader set of political practices of industry actors to influence public health policy, research, and practice. It is important to learn about and counter these practices as they risk shaping scientific standards to suit the industry’s interests rather than public health ones.

Actors in the tobacco, alcohol, and ultra-processed food industries use a broad range of political strategies to protect and expand their markets [ 1 , 2 , 3 , 4 ]. These practices include direct influence on public health policy, and more subtle actions like cultivating support from communities and the media [ 1 , 2 , 3 , 4 ]. The shaping of science is one of these political practices [ 5 , 6 , 7 , 8 ], as science can be used to influence policy [ 9 , 10 , 11 ]. Studies that link the consumption of harmful products to ill-health, or those which provide evidence on the effectiveness of a policy that limits consumption, are systematically questioned, attacked, or undermined by companies and third parties working on their behalf [ 5 , 6 , 7 , 8 ]. Industry actors are also shaping the research agenda by funding commercially-driven science (research supported by the industry) to support their products or practices [ 12 ].

When evidence emerged about cigarette smoking’s harmfulness in the 1960s, tobacco companies mounted an attack on science to bury that evidence [ 13 ]. However, the tobacco industry understood that it could not credibly question scientific evidence criticizing its products. In the 1980s and 1990s, tobacco companies developed a “ sound science ” program, hiring respected academics and scientists and using third parties to deny secondhand smoke’s harmful effects [ 14 , 15 ]. Through this program, tobacco companies intended to shape scientific proof standards so that no study could prove that secondhand smoking was harmful [ 14 , 15 ]. In response, in 2003, the World Health Organization adopted a Framework Convention on Tobacco Control, in which Article 5.3 insulated public health policymaking from the tobacco industry [ 16 ]. Although the implementation of Article 5.3 is successful in some contexts [ 17 ] and could serve as a model for other industries [ 18 ], the tobacco industry is still able to participate in the development of principles for using scientific evidence in policy along with academics and government officials [ 19 ].

Similar to the tobacco industry, the food industry also shapes science, through the funding and dissemination of research and information serving its interests and criticizes evidence that may thwart these interests [ 3 , 12 , 20 ]. The food industry established and funded scientific-sounding groups such as the International Life Science Institute (ILSI), set up in 1978 by a former executive from Coca-Cola, to push for its agenda in the scientific and policy spaces [ 21 ]. ILSI also represented tobacco companies in the 1980–90s [ 22 , 23 ]. ILSI is currently composed of fifteen branches [ 24 ], each with a broad range of industry and academic members. The global branch of ILSI is governed by a Board of Trustees that mixes employees from the food industry, including the agribusiness sector (Ajinomoto, PepsiCo, Cargill) and academics [ 25 ]. Industry-supported research is also subject to peer-review by the industry itself. ILSI has its own journal, Nutrition Reviews, amongst the most popular journals in nutrition [ 26 ]. A recent study found that Nutrition Reviews has the highest proportion of articles with industry involvement (a quarter of all articles from that journal) amongst the top top 10 journals in nutrition [ 26 ].

From a public health perspective, somehow, the food industry’s involvement in science and policy is not seen as controversial and harmful as that of the tobacco industry [ 27 , 28 ]. Some think there is a space for collaboration with that industry, as illustrated in a recent study that tried to build consensus on the interactions between researchers and the food industry [ 29 ]. When criticism of the food industry’s involvement in science grew in the 2000s [ 30 , 31 , 32 ], ILSI developed guidelines on conflicts of interest (COI) and scientific integrity [ 20 ]. These principles call for the involvement of all actors in science, including those from industry actors, and are, not surprisingly, silent on the risks associated with such engagement with industry actors [ 20 , 33 ]. While there is growing evidence of the food industry’s involvement in science on nutrition and physical activity, little is known of their broader influence on the very principles of scientific integrity.

Our objective was to study the extent of the food industry’s involvement in developing scientific integrity principles, with a case study of ILSI as a key actor in that space.

We conducted a qualitative document analysis between February–November 2020, where we triangulated multiple sources of information. We started with initial searches based on an existing scoping review on principles for the interactions between researchers and the food industry. MH conducted searches on the industry’s websites, their social media, and in the Food Industry Documents Library of the University of California, San Francisco [ 34 ], an archive containing previously secret internal industry documents. We also used documents from existing freedom of information (FOI) requests made by U.S. Right to Know, a nonprofit investigative public health group. MH and GR independently conducted an initial review of the material for their inclusion against our research objective. MM led the searches on Web of Science and data analysis for all sources of information.

We searched these sources for information related to the development of principles, codes of conduct, frameworks, standards, or other scientific integrity guidelines and responsible research. An analysis of the content and implementation of those principles was beyond the scope of our study.

For the present study, we used the definition of ‘scientific integrity’ from the U.S. National Research Council: “ Integrity characterizes both individual researchers and the institutions in which they work. For individuals, it is an aspect of moral character and experience. For institutions, it is a matter of creating an environment that promotes responsible conduct by embracing standards of excellence, trustworthiness, and lawfulness that inform institutional practices. For the individual scientist, integrity embodies above all a commitment to intellectual honesty and personal responsibility for one’s actions and to a range of practices that characterize responsible research conduct ” [ 35 ].

Initial identification of industry actors

In 2019, MM conducted a backward search, using a recent scoping review by Cullerton et al. and a commentary published in response to that review [ 36 , 37 ]. The scoping review was purposively selected for our initial searches because it represented the most recent and comprehensive summary of existing principles “ to guide interactions between population health researchers and the food industry ” [ 36 ]. The publications identified in the scoping review included work that was funded independently but also work that was supported by the food industry. A response to that review identified additional material from the review sponsored by the food industry [ 37 ]. These publications constituted our initial samples of scientific integrity documents developed with industry support (Table  1 ). This initial sample only included documents where the food industry had direct involvement, through the declarations of interest sections or funding acknowledgments sections or institutions to which the authors were affiliated. By ‘food industry’, we meant any actor along the food supply chain, in the production of raw material, manufacturing, marketing, retailing, and public relations sectors, as well as third parties working on their behalf. We only included those publications that proposed scientific integrity principles, not those broadly discussing the industry’s involvement in science, without providing any guidelines (such as [ 47 , 48 ]). We also excluded publications on the implementation of such principles at the organizational level, as falling outside the present study’s scope.

With these initial searches, we identified five documents: three scientific articles and two reports. The North American branch of ILSI published four of the five publications, with support from large US-based food manufacturers. Two authors from ILSI also published a fifth article with an author from DuPont Nutrition (DuPont), a dietary supplement manufacturer for the food industry. Therefore, we decided to restrict our following searches to ILSI and DuPont, as they were the only industry actors publishing in the peer-reviewed literature on the topic of scientific integrity.

Systematic searches on web of science

As a second step, we conducted a literature search to identify further publications on the topic by the ILSI and DuPont, based on the findings of our initial search. On 14 November 2020, MM searched Web of Science Core Collection (Web of Knowledge interface) (our search strategy is available in Additional file  1 ).

We used the terms (principle* or guid* or ‘codes of conduct’ or framework* or standard* or transparen* or fund*) AND (partner* or integrity or ethic* or inter*) as identified in the titles of publications. We refined the search to publications from ILSI and DuPont, as stated in the declarations of interest sections; funding acknowledgments sections; or institutions to which the authors were affiliated. We had no restriction on the publication time.

All data were extracted from WoS and managed on Mendeley. The publications retrieved from that search were screened for eligibility, based on their titles and abstracts. All data were independently double-screened by A.C. There was no disagreement on the inclusion of documents.

From these systematic searches, no relevant work by DuPont was identified; we, therefore, further restricted our searches for the next steps and focused on ILSI only.

Industry websites and twitter accounts

MH, with support from EC, identified all websites and Twitter accounts of ILSI Global and its fifteen branches. ILSI’s websites are presented in Additional file  2 . MH searched these websites, and social media accounts, for information related to the development of scientific integrity principles. MM then analyzed all data. Our data collection was limited to data available on these websites, and we did not use internet archives to retrieve data that may have been published and then subsequently deleted. In February 2021, ILSI North America transformed into the “Institute for the Advancement of Food and Nutrition Sciences” (IAFNS), a “a non-profit organization that catalyzes science for the benefit of public health” [ 49 ]. The URLs for ILSI NA’s webpages in Additional file 2 now redirect to the new IAFNS website. The webpages consulted during data collection could still be consulted using internet acrchives tools like the Wayback Machine [ 50 ].

Archive from industry documents library

Between February and July 2020, MH searched food industry documents in the Food Industry Documents Library of the University of California, San Francisco [ 34 ], using standard snowball search methods [ 51 ]. Initial keyword search terms included ‘ILSI’, ‘International Life Sciences Institute’, ‘research integrity’, and ‘research transparency’. Twenty-one documents between 2012 and 2018 were located, with most records dated between 2015 and 2017. Documents were screened (MH) and analyzed (MH and MM) for the direct mentioning of information outlining ILSI’s development of scientific integrity principles. Sixteen documents were deemed relevant based on how applicable their contents were to the research objective.

Documents from existing FOI requests

Additionally, we drew upon nine U.S. federal and state FOI data sets to triangulate our other sources of information: (1) Louisiana State University (Tim Church); (2) University of Colorado (John Peters); (3) Louisiana State University (Peter Katzmarzyk); (4) Texas A&M University (Joanne Lupton); (5) Centers for Disease Control and Prevention (Maureen Culbertson); (6) University of Colorado (James Hill); (7) University of South Carolina (Steven Blair); (8) Louisiana State University (Pennington Biomedical Research Center); (9) U.S. Department of Agriculture (David Klurfeld). U.S. Right to Know filed these FOI requests between 17 July 2015 and 27 December 2017. The requests covered issues regarding sugar sweetened beverages, candy and food companies, and their public relations firms, trade associations, and other allied organizations. The identification of relevant documents for our study was made by GR and his colleague Rebecca Morrison, for their relevance to our research objective.

In November 2020, MM reviewed all data from the sources mentioned above and mapped the actors, timeline of events, and other relevant information related to the food industry’s involvement in the development of scientific integrity principles. In the present manuscript, we present a narrative synthesis of our findings. All authors reviewed the analysis and presentation of findings in the manuscript. We had regular meetings during data collection and analysis, and any disagreement was resolved through discussion within the team. Our existing knowledge informed our analysis of industry influence on science. In the present document, we use the acronym ‘ILSI’ to refer to ILSI North-America, unless otherwise stated. In the results section, we use a code starting with the letter A to refer to our data, all available in Additional file  3 .

Our Web of Science systematic searches yielded 42 publications, 33 of which were excluded as not meeting our inclusion criteria. In addition, one article from 2014, by an author from DuPont, discussed funding by the food industry but did not provide any specific guidelines, so it was excluded [ 52 ]. There were eight publications relevant to our research objective on WoS, for our sample of food industry actors. Amongst these eight publications, five were already identified through our initial searches (Table 1 - [ 38 , 44 , 46 ]) with three copies of the same article by ILSI published in different scientific journals simultaneously. The three other studies were also published by ILSI [ 53 , 54 , 55 ]. With our searches in internal documents, we found two other publications from the food industry on scientific integrity, both supported by ILSI [ 56 , 57 ].

In total, we found eight scientific papers from ILSI on scientific integrity, published between 2009 and 2019. In Nov 2020, when writing the current manuscript, these documents were, when combined, cited 364 times (Google Scholar). ILSI also presented its principles in scientific events, reports, and other platforms, as described in Table 1 and below.

Additional file  4 presents a list of authors who published these scientific papers: 63 authors in total, 24 (38%) were from the food industry (as disclosed in the publications). Other authors were from academia, government agencies, and professionals associations, amongst other institutions (see Additional file 4 ). The majority of the authors were U.S.-based (70%). Five individuals authored four publications (the maximum for a single author), four of them from ILSI and one from academia.

Of note, ILSI promotes these publications on its website, stating, “ILSI North America has become a leader in scientific integrity and public-private research partnerships for the food and nutrition community. Our work has been published in peer-reviewed journals, endorsed by Federal agencies and professional nutrition and food science societies, and cited broadly throughout the scientific community ” [ 58 ].

Figure  1 summarizes our findings.

Food industry’s development of scientific integrity principles overtime

In the period 2009–2015, ILSI published articles on conflicts of interest that mainly covered food science, of relevance to food companies, and nutrition, a sub-field of health sciences. During that period, the target audience was researchers. In 2013, a shift occurred, from publishing recommendations on conflicts of interest and the good conduct of research, particularly at the individual and study levels, to proposing guidelines for public-private partnerships (PPP), assuming that PPP would benefit nutrition research. Then, from 2015, ILSI began to target a broader audience, outside academia, such as government agencies and civil society organizations, in its development of scientific integrity principles. At that time, ILSI also started targeting the entire scientific field, and not only the area of nutrition and health.

2007–2012: addressing COI in food science and nutrition research

Based on the information we collected, ILSI’s development of scientific integrity principles started in 2007. At that time, the organization “ initiated a program to address COI issues ”, with the rationale that “ despite a wealth of benefits industry sponsored research and science programs have provided, there continues to be significant public debate on the credibility of such support ” [A1]. Over the period 2007–2012, ILSI published COI principles focusing on food science and nutrition research. These publications resulted from different meetings of individuals from the food and agro-industries and academia. At that time, ILSI published on financial conflicts and scientific integrity in food science and nutrition research [ 38 , 39 , 40 , 41 , 42 ].

The first publication is from 2009. The paper originated from a working group at ILSI, the “COI and scientific integrity” working group, and was supported by ten food companies through “ educational grants ” to ILSI [ 38 , 39 , 40 , 41 , 42 ]. Its authors included a mix of employees from ILSI, food companies (Coca-Cola, Kraft, PepsiCo, Cadbury, and Mars), and academics in food science, nutrition, and pediatrics from the U.S. and Canada [ 38 , 39 , 40 , 41 , 42 ]. ILSI said it published this material in six different scientific journals [A2], although we found no trace of the publication in the Journal of Food Science. The article was published in Nutrition Reviews, a journal run by ILSI, the only one of the six journals where the article underwent peer-review. The Academy of Nutrition and Dietetics (formerly American Dietetic Association), who published one copy in its journal, and the American Society for Nutrition (ASN), who published three copies in its American Journal of Clinical Nutrition, Journal of Nutrition, and Nutrition Today, are known to be industry-friendly and receive funding from the food industry [ 20 , 59 , 60 ], which may explain their willingness to publish the paper. The 2009 publication was also adapted, in 2012, into a report of the International Union of Food Science and Technology [ 38 ].

In 2011, the ILSI Europe’s Functional Foods Task Force published “ guidelines for the design, conduct and reporting of human intervention studies to evaluate the health benefits of foods ” [ 53 ]. The paper named 38 food (including agribusiness) and pharmaceutical companies as members of the taskforce [ 53 ]. Amongst the list of authors of the article, six were from the food industry (ILSI, Danone, DuPont (Danisco), Nestlé, and Beneo), three were consultants, and five were academics [ 53 ].

In a 2012 letter to ILSI members, Rhona Applebaum, then ILSI’s President and Coca-Cola’s chief health- and science officer, concluded ‘ the program has been highly successful in developing “guiding principles” for industry funding of research ’ [A2]. The success was in the guidelines being “ endorsed by the leadership of three major professional societies. Results of this work have been published in six different peer-reviewed journals and presented at numerous scientific conferences ” [A2]. In that same correspondence, Applebaum sent a list of ILSI’s publications on scientific integrity, where one additional article published in 2011 was included. The latter discussed funding in nutrition research and was published with support from ILSI [ 56 ]. The publication was written by four individuals: two from the AND, a consultant, and an academic [ 56 ].

2012–2015: pushing for public-private partnerships in nutrition research

The period 2012–2013 was a turning point for ILSI, where the discussion on COI in science shifted to the use of science in policy. In her 2012 letter mentioned above to ILSI members, Applebaum stated that there was a “ demand by some that all industry-funded research, whether conducted at contract research organizations or universities, be denied consideration in the formulation of public policy. Furthermore, scientists who have conducted industry-funded research have been barred from serving on public advisory committees ” [A2]. Applebaum, therefore, called ILSI’s food companies members for the “ development of criteria for participation on scientific advisory panels and establishment of appropriate protocols for successful public/private partnerships to advance public health ” [A2]. Food companies were asked to contribute to this task by paying a fee of US$10,000 each [A2].

Therefore, a series of ILSI’s publications on PPP appeared in the scientific literature between 2012 and 2015. In 2012, ILSI’s “ COI and scientific integrity ” working group produced two publications. The first provided suggestions on selecting experts to advise in public policy decision making [ 57 ]. The second publication, published in Nutrition Reviews, proposed “ principles for building public-private partnerships to benefit food safety, nutrition, and health research ” [ 44 ]. The authors of both publications were a mix of academic experts on the topic, industry employees, and ILSI’s staff.

In January 2014, in a personal communication to prominent physical activity researchers from the US, Applebaum explained that she “ asked ILSI to consider drafting a set of principles on civil discourse in science by scientists similar to what they have done for conflict of interest and public private partnerships .” She also mentioned: “ There must be a set of guidelines to avoid the current demonizing. They [ILSI] had also been asked to work on principles re selection on gov’t panels since our own U.S. gov’t has raised the issue of working w/ industry as a criterion for non-inclusion ” [A4].

This idea soon translated into concrete action. ILSI first published an article that “ offers counsel on meeting [challenges] in communicating about the work of emerging public-private partnerships ” [ 61 ]. This article does not set principles on scientific integrity per se. Still, it is to be understood as part of ILSI’s work in promoting PPP as a means to pursue industry interests.

In 2014, ILSI also started working with third parties on PPP principles, thus accelerating the translation of their work into practice and policy. ILSI proposed to “have a manuscript to share with FDA [U.S. Food and Drug Administration] on best practices for advisory committees”, when the FDA was developing its own COI guidelines [A9].

In parallel, during late 2013, the ASN “ approached ILSI North America to collaborate ” [A109] on activities that would “ stimulate the expansion, accessibility, and acceptance of PPPs by unifying and moving existing principles for food and nutrition research PPPs forward ” [A49]. The ASN convened representatives from the U.S. Department of Agriculture, ASN, Academy of Nutrition and Dietetics, American Heart Association, Centers for Disease Control and Prevention, FDA, Grocery Manufacturers Association, and National Institutes for Health, amongst others [A50]. An individual from the U.S. Department of Agriculture, Klurfeld, and Rowe, a consultant for ILSI, co-chaired a newly formed “ Working Group on Conflict of Interest & Scientific Integrity ” [a name similar to that of ILSI’s “COI and scientific integrity” working group] [A10–1, A14–5]. In 2014, the working group had regular emails, calls, and a face-to-face group meeting in December [later called the “ COI Summit Consortium ”], to agree on a set of PPP principles [A10–5, A29–30]. An ad-hoc steering group was also formed with three USDA staff and a consultant from ILSI, and an ASN staff member [A29].

The whole project was formally led through a “ U.S. government-wide Interagency Committee on Human Nutrition Research ” [A29]. It was formed in 2011 and included a component on PPP, “ in part in response to [a] 2011 Presidential memo directing agencies to develop public-private partnerships in areas of importance to an agency’s mission ” [A29]. In our FOI documents and when justifying the PPP, the ASN made further reference to President Obama, who “ issued a Presidential memorandum in July 2014 encouraging government at all levels to work with private partners on developing infrastructure to lay the foundation for future prosperity ” [A41].

In May 2014, an employee from ILSI sent an email to lead American researchers and employees of federal agencies (U.S. Government Accountability Office and National Institutes for Health), describing the proposed outcome of the newly formed PPP project, a “ summit or collection of major professional societies and federal agencies coming together in support of PPP principles ( … ). At the conclusion of the summit, the professional societies would agree to a consensus statement on private funding for research and general acceptance of principles for PPPs ( …). it might be helpful for societies who publish journals to have their editors participate in summit ” [A8].

Soon after, in 2015, a peer-reviewed paper outlining the PPP principles in food and nutrition research was published in the Journal of Clinical Nutrition [ 46 ] and “ an excerpt of the article appeared in the Journal of the Academy of Nutrition and Dietetics, Journal of Food Science, Nutrition Reviews, and Nutrition Today ” [A66]. In the publication, the authors made clear that the group took “ the ILSI North America published principles as a starting point ” [ 46 ], given that “ most reports were not readily accessible in the public domain until, in 2013, a group organized by ( … ) ILSI North America ( …) published proposed criteria ” [ 46 ]. The principles were endorsed by the “ ASN, Academy of Nutrition and Dietetics, American Gastroenterological Association, Institute of Food Technologists, International Association for Food Protection, and ILSI, collectively representing approximately 113,000 professionals ” [A31]. The American Public Health Association declined to endorse the principles but did not justify its decision [A24].

On 16 June 2015, the PPP principles were launched at the National Academy of Sciences. ILSI, in its internal communication, talked of the event and principles as its own: “ There is a meeting today at the National Academies to discuss [PPP] as defined by work that ILSI North America did. ASN and U.S. Department of Agriculture organized the meeting and we expect a number of scientific organizations to adopt the ILSI North America principles ” [A26, A34]. Speakers at that event included the U.S. Department of Agriculture Chief Scientist and Under Secretary, Research, Education, and Economics Dr. Catherine Woteki (keynote address), as well as an ILSI consultant, and an Institute of Medicine Senior Scholar, amongst others [A15, A31].

ILSI and the ASN also had other avenues for disseminating the PPP principles, as detailed in Table  2 . The ASN and the Academy of Nutrition and Dietetics were also keen to support a “ Conclave on public-private partnerships ”, where a Declaration would be issued “ to provide a transparent and actionable framework for interested public and private organizations that will minimize external criticism ” [A110].

Therefore, by having built its own literature on COI principles, scientific integrity, and PPP, and by reaching out to potential allies outside the industry, ILSI naturally became a central and pivotal actor in that discussion.

Hereafter, ILSI took yet another step in disseminating its principles into the scientific and policy spheres, beyond that of nutrition research.

2015–2019: beyond nutrition, influencing the very principles of scientific integrity

Hence, after having developed principles for research, and having these principles used to create PPP, ILSI started to evaluate the efforts made by a range of actors to implement scientific integrity principles.

Indeed, in parallel to the work undertaken by the “ U.S. government-wide Interagency Committee on Human Nutrition Research ” working group, ILSI, in 2015, through its own working group, proposed to “ seek a broader group of collaborators than we have previously worked with in order to have a greater impact; ones that have impeccable reputations and are not focused on only one area of science. Possible candidates are: a. American Association for the Advancement of Science; b. Association of Public and Land-grant Universities; c. Association of American Universities; d. The National Academies ” [A80]. ILSI’s working group also suggested that ILSI’s focus “ should be on implementation of these principles/best practices” [A80]. The group also proposed that when the COI Summit Consortium “reconvene [s] in two years to reassess the PPP principles ( …) ILSI North America could introduce the principles/best practices for scientific integrity and seek endorsement from the nutrition, food science, and food safety professional societies ” [A80].

As part of that work, in 2017, ILSI set up an “ Assembly on Scientific Integrity ”, whose steering committee included three academics from the University of Illinois, the University of Wisconsin, and Tufts Medical Center, and five employees from Coca Cola, General Mills, Abbott Nutrition, Ocean Spray Cranberries and Biofortis [A79]. The Assembly was made of “ ILSI North America Board of Trustees, all Member Companies of ILSI North America, and the ILSI North America Canadian Advisory Committee ” [A58, A84]. The Assembly was also “ hoping to include government liaisons in the Assembly on Scientific Integrity and it is likely that the ILSI North America Mid-Year meeting in Washington, DC is a better location for government officials to be able to join in-person ” [A107]. In 2017, the budget of the Assembly was US$122,000 [A107].

Then, two authors from ILSI and one from academia, also on the newly formed steering committee and author of other ILSI publications, produced a review of “ efforts by federal agencies, foundations, nonprofit organizations, professional societies, and academia in the United States ” [ 54 ]. The review was then translated into a Resource Guide and regularly updated, and similar activity was planned for Canada [A85–6, A98]. Here, the focus was not on food science and nutrition anymore, and the article reported on efforts made by a broad range of institutions like the Centers for Disease Control and Prevention, the Committee on Publication Ethics, the Institute Of Medicine, and the Laura and John Arnold Foundation [ 54 ]. The article was published in Critical Reviews in Food Science and Nutrition. ILSI seems to have opened a discussion that is meant to last in that space by inviting readers to “ help keep this document current by pointing out areas that need to be expanded or updated or additional organizations that should be included ” [ 54 ].

ILSI’ scientific integrity working group also proposed to “develop and publish a second paper in collaboration with [the American Association for the Advancement of Science, the Association of Public and Land-grant Universities, and the Association of American Universities] that builds on the first manuscript ( …) to establish the first” rulebook “ on scientific integrity ” [A81]. ILSI convened a meeting in March 2017, where a broad range of actors would discuss the new scientific integrity principles [A86, A101]. The new “ Scientific Integrity Consortium ” was made of “ representatives from four U.S. government agencies, three Canadian government agencies, eleven professional societies, six universities, and three nonprofit scientific organizations ” [A57, A86, A101]. The meeting was organized at the National Academies of Science, Engineering and Medicine as part of the “ Government University Industry Research Roundtable ” [A86, A101], in the same venue used for the launch of the 2015 PPP principles. The group then continued to meet virtually and in-person in 2017 and 2018 [A57, A69, A86]. The “ Scientific Integrity Principles and Best Practices ” were finally published in 2019 in Science and Engineering Ethics [ 55 ], reaching a broader audience than merely the nutrition space. ILSI was satisfied that “ the convening of the Scientific Integrity Consortium was a significant step for ILSI North America in building upon our work on scientific integrity and engaging the scientific community beyond the nutrition and food safety community ” [A86]. The long COI section in that publication reports on the many interactions between several of its authors and industry actors [ 55 ]. Here again, the Consortium used ILSI’s 2017 findings “ as the basis of the discussion and reconstructed them to form the final set of recommended principles and best practices for scientific integrity ” [ 55 ], in combination to some work of the American Society for Microbiology on that topic.

The scientific integrity principles, like those for PPP, were disseminated through different scientific events, in what ILSI called a “ roadshow ” [A104] (see Table  3 for a list of events), with the goal of “ educating attendees (with a focus on young researchers/post docs) on the components of scientific integrity ” [A81]. This time, the audience reached beyond that of nutrition.

In some of these events, ILSI’s official role in developing the principles was presented as a Consortium member, not its convener [A71]. In October 2017, ILSI shared its Resource Guide directly with the World Conferences on Research Integrity Foundation, who considered using the material for their work [A73, A87]. ILSI, at that time, was seeking to collaborate with the Foundation to further expand its principles globally [A73, A87]. ILSI also planned to develop a training module to implement the new scientific integrity principles and “ a certification program or accreditation ( …) for individuals or organizations to certify their use of the principles and best practices. ( …). It would be beneficial if government agencies would require the certification or accreditation in order to apply for a grant ” [A106].

ILSI is now planning to “ share what we’ve learned with the entire federation of global ILSI entities ” [A67]. ILSI NA’s 2019 Mid-Year Science Program included a presentation on the “ Benefits of More Transparent Research Practices and Bias Reduction Tools ” from a speaker from the Center for Open Science [A59]. ILSI started collaborating with that Center in 2017 [A74, A78]. In 2017 as well, ILSI Argentina formed a new Scientific Integrity Group [A107]. In 2019, the Brazilian branch of ILSI put the question of scientific integrity in the food area as the main topic of its annual congress [A64], with speakers from different Brazilian federal agencies and universities. That same year, an academic from Chile gave a presentation on scientific integrity for the South Andean branch of ILSI [A65].

ILSI continues to try to drive the discussion on scientific integrity in the present COVID-19 pandemic context. In November 2020, ILSI held a webinar where “ invited experts [discussed] some of the challenges that exist for scientists and journals when attempts are made to correct the scientific record - through retractions, corrections or letters/commentaries ”, in response to the “ heightened visibility of retracted publications during the COVID-19 pandemic ” [A68]. The experts in question included some of the authors of the ILSI’s publications presented in our study.

In our study, we found that ILSI is a leading actor, not only in the food industry but more broadly in the scientific community, on the development of scientific integrity standards and principles. Internal and FOI documents revealed the food companies’ motives in developing scientific integrity principles. Food companies have joined forces through ILSI, funded its first activities on COI, and have 38% of the authorship of its scientific integrity publications. We have shown that ILSI built a niche literature, one that would become useful for the food industry, when criticism of its funding of researchers emerged in the U.S. in the mid-2000s [ 30 , 32 ]. ILSI first focused on COI in food science and nutrition at the individual and study levels, from 2007. Because the literature was scarce on that topic, its publications were used and cited in ILSI’s and others’ further work on COI, scientific integrity and PPP, beyond the field of nutrition and food science. In the past few years, ILSI started to shape the very principles of scientific integrity then and to propose that government agencies, professional associations, non-for-profits, and others, adopt these principles. In the process, ILSI built a reputation in the scientific integrity space. Our study found that ILSI proposed a compulsory certification or accreditation, based on the adoption of its scientific integrity principles, for anyone willing to apply for a research grant. If that were to happen, then ILSI could make it impossible to avoid adhering to its principles. Transparency is often prioritized as per ILSI’s current scientific integrity principles and by government agencies and scientific journals. Transparency should, however, be understood as only one aspect of scientific integrity. It is reasonable to promote the involvement of a broad range of actors in science and to promote good principles for the use of evidence in policy, but ILSI’s work on scientific integrity ignores the risks associated with accepting industry funding [ 20 , 37 ] and fails to provide guidelines to protect from these risks [ 19 , 37 ].

It may be that not all individuals and organizations cited in our manuscript were aware that ILSI was founded and is funded by food companies, and that it is food companies that are shaping scientific integrity principles. ILSI, in its publications and communications, presents itself as an independent organization. However, in several of the documents consulted for our study, such as minutes of meetings and emails, and in the scientific publications mentioned here, industry actors were omnipresent. This reveals a state of affairs where the food industry is seen as a legitimate actor in science and policy and where academics see no problem in working with industry actors [ 28 ]. In the very process of developing scientific integrity principles, food companies may use their connections with these reputable individuals and organizations to further their influence on science and policy [ 62 , 63 ].

What we describe here will not be a surprise for ILSI, as they are transparent on these activities, the researchers they fund and indeed promote these principles widely. Some of the information we found during our study was indeed made public. However, internal and FOI documents revealed the true intentions of ILSI behind their development of scientific integrity principles.

This study is novel and builds on several sources to triangulate its findings. Internal industry documents provide a unique behind the scenes look at industry activity and reveal and expose industry behavior rather than speculating about it. This study also has limitations. First, it was beyond the article’s scope to examine all the COI that the individuals identified in our study had with ILSI or other actors in the food industry. Hence, it is highly likely that their relationships extend beyond their authorship on the publications identified here. It is also possible that these authors have published on scientific integrity elsewhere without disclosing their links with ILSI and the food industry. For example, Rowe, a consultant for ILSI on scientific integrity since 2009, published in 2015 a summary of the activities undertaken by ILSI in that space, in one of the chapters, entitled “Principles for Building Public/Private Partnerships to Benefit Public Health”, in the book “Integrity In The Global Research Arena” [ 64 ]. In the chapter, there is no reference to the fact that Rowe worked for ILSI and that ISLI has ties with food industry actors. Nevertheless, a broader extent of industry participation would not change the essence of the current findings. Second, this study neither evaluated the content and scientific merit of the scientific integrity principles developed by ILSI and others, nor their implementation. Lastly, our primary focus was ILSI’s work, as our initial searches pointed in that direction, hence potentially leaving out some other work on scientific integrity from other companies and industries, like the pharmaceutical industry. This could be the subject of future investigations.

Our study goes beyond what we know of the food industry’s nutrition and physical activity research funding. It shows that the food industry, like the alcohol and tobacco industries [ 19 ], tries to influence science’s very principles, such as scientific integrity and the good conduct of research. Similar to the findings of Ong and Glantz, published 20 years ago on the tobacco industry, the activities described in our paper reflect “ sophisticated public relations campaigns controlled by industry executives ( …) whose aim is to manipulate the standards of scientific proof to serve the corporate interests of their clients ” [ 14 ]. Importantly, public health professionals should understand the activities presented here as only one of many practices through which the food industry tries to influence science and policy [ 15 ]. This reinforces the call for considering researching the political practices undertaken across industries [ 65 ].

ILSI’s work on scientific integrity, conflicts of interest and public-private partnerships waters down independent work in that space, puts profits before science, and undermines efforts to address undue influence of industry actors on public policy, research, and practice. The industry-established principles have already shaped the evidence on scientific integrity. In the scoping review we identified as a starting point for our searches by Cullerton et al. [ 36 ], 14 of the 54 documents included in the review were funded or had involvement of the food industry, despite the clear vested interests that the food industry has in that discussion [ 37 ]. Mc Cambridge et al. recently wrote that “ calls for research integrity reflect core values of the research community. They should not be used as instruments to undermine science or to assist harmful industries ” [ 19 ]. Therefore, it is crucial that the public health community monitors this work done by ILSI and others and recognizes that seemingly independent organizations like ILSI may represent industry’s interests [ 15 , 19 ]. This is even more crucial now that ILSI North America transformed itself nto the “Institute for the Advancement of Food and Nutrition Sciences”, a new organization that lacks transparency about its ties with the industry and whose current and future activities remain to be studied [ 49 ]. It risks shaping public agencies’ work, which may not be aware of the issues discussed in our paper. The literature we have described here must be understood not to have emerged from within the dietetics or nutrition or even medical professions, but rather from the food industry [ 14 ].

Availability of data and materials

All data generated or analyzed during this study are included in this published article [and its supplementary information files].

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Authors on ILSI’s publications on scientific integrity, 2009-2019.

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Mialon, M., Ho, M., Carriedo, A. et al. Beyond nutrition and physical activity: food industry shaping of the very principles of scientific integrity. Global Health 17 , 37 (2021). https://doi.org/10.1186/s12992-021-00689-1

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Application of Artificial Intelligence in Food Industry—a Guideline

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Artificial intelligence (AI) has embodied the recent technology in the food industry over the past few decades due to the rising of food demands in line with the increasing of the world population. The capability of the said intelligent systems in various tasks such as food quality determination, control tools, classification of food, and prediction purposes has intensified their demand in the food industry. Therefore, this paper reviews those diverse applications in comparing their advantages, limitations, and formulations as a guideline for selecting the most appropriate methods in enhancing future AI- and food industry–related developments. Furthermore, the integration of this system with other devices such as electronic nose, electronic tongue, computer vision system, and near infrared spectroscopy (NIR) is also emphasized, all of which will benefit both the industry players and consumers .

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Introduction

Artificial intelligence (AI) is defined as a field in computer science that imitates human thinking processes, learning ability, and storage of knowledge [ 1 ,  2 ]. AI can be categorized into two types which are strong AI and weak AI. The weak AI principle is to construct the machine to act as an intelligent unit where it mimics the human judgments, while the strong AI principle states that the machine can actually represent the human mind [ 3 ]. However, strong AI does not exist yet and the study on this AI is still in progress. The gaming industry, weather forecasting, heavy industry, process industry, food industry, medical industry, data mining, stem cells, and knowledge representation are among the areas that have been utilizing AI methods [ 4 – 11 ]. AI has a variety of algorithms to choose from such as reinforcement learning, expert system, fuzzy logic (FL), swarm intelligence, Turing test, cognitive science, artificial neural network (ANN), and logic programming [ 3 ]. The alluring performance of AI has made it the most favorable tool to apply in industries including decision making and process estimation aiming at overall cost reduction, quality enhancement, and profitability improvement [ 7 ,  12 ].

As the population in the world is rising, food demand is predicted to rise from 59 to 98% by 2050 [ 13 ]. Thus, to cater for this food demand, AI has been applied such as in management of the supply chain, food sorting, production development, food quality improvement, and proper industrial hygiene [ 14 – 16 ]. Sharma stated that the food processing and handling industries are expected to grow about CAGR of 5% at least until 2021 [ 15 ]. ANN has been used as a tool in aiding real complex problem solving in the food industry according to Funes and coworkers [ 17 ], while based on Correa et al., the classification and prediction of parameters are simpler when using ANN, which leads to higher usage demand of ANN over the past years [ 18 ]. Besides, FL and ANN have also acted as controllers in ensuring food safety, quality control, yield increment, and production cost reduction [ 19 ,  20 ]. AI technologies have also known to be beneficial in food drying technology and as process control for the drying process [ 21 – 23 ].

Previous studies have shown many usages of AI in food industries focusing on individual target and aims. A study has been conducted on the various ANN applications in food process modeling where it has only highlighted the food process modeling using ANN [ 24 ]. Apart from that, the implementation of AI such as ANN, FL, and expert system in food industries have been reviewed but specifically focusing on the drying of fresh fruits [ 23 ]. A review has been conducted on how food safety has been one of the main concerns in the food industry which leads to the development of smart packaging systems to fulfill the requirements of the food supply chain. Intelligent packaging monitors the condition of foods to give details on the quality of the food during storage and transportation [ 25 ]. Another study reviewed on intelligent packaging as a tool to minimize food waste where about 45 recent advances in the field of optical systems for freshness monitoring have been reported. Meat, fish products, fruits, and vegetables were covered in the study as they are the most representative fields of application [ 25 ]. Few different studies have been conducted on intelligent packaging, and these studies proved that the usage of intelligent packaging systems plays an important role in the food factory in the context of the food chain as they are able to monitor the freshness of food products and crops [ 23 ,  26 – 30 ].

There are also several other studies that have been conducted on the application of AI and sensors in food; however, the coverage is rather limited. Therefore, a comprehensive review that assembles all AI applications in the food industry as well as its combinations with appropriate sensor will be a great advantage, all of which are unavailable as to the knowledge of the author. Such review will assist in gathering the advantages, limitations, and methodologies as a one-stop guideline and reference for food industry players, practitioners, and academicians. To be exact, different types of AI and their recent application in food industries will be highlighted which comprises several AI techniques including expert system, fuzzy logic, ANN, and machine learning. In addition, the integration of AI with electronic nose (E-nose), electronic tongue (E-tongue), near infrared spectroscopy (NIRS), and computer vision system (CVS) is also provided. This paper is organized as follows. The introduction of AI is explained in the first section followed by the application of different types of AI in the food industry. Following that, the fusion of the AI with the external sensors in the food industry is presented. In the latter part, a critical review is conducted where discussion on the main application of the AI algorithms in the food industry is carried out. A flowchart is presented to assist the researchers on establishing the most appropriate AI model based on their specific case study. Then, the trends on the application of AI in the food industry are illustrated after that section. Finally, a brief conclusion is discussed in this paper.

AI in Food Industry

The application of AI in the food industry has been growing for years due to various reasons such as food sorting, classification and prediction of the parameters, quality control, and food safety. Expert system, fuzzy logic, ANN, adaptive neuro-fuzzy inference system (ANFIS), and machine learning are among the popular techniques that have been utilized in the food industries. Prior to AI implementation, studies related to food have been going on over the years to educate the public about food knowledge as well as to improve the final outcomes related to food properties and the production of foods [ 31 – 36 ]. A lot of benefits can be obtained by using the AI method, and its implementation in the food industry has been going on since decades ago and has been increasing till today [ 37 – 39 ,  31 , 32 ]. Nevertheless, this paper will focus on the application of AI in food industries from the year 2015 onwards since tremendous increase and innovation are seen in the implementation recently. It is worth noting that several methods such as partial least square, gastrointestinal unified theoretical framework, in silico models, empirical models, sparse regression, successive projections algorithms, and competitive adaptive reweighted sampling which have been used for prediction and enhancement of the food industries are not discussed here; instead it is narrowed down to the wide application of AI in the food industry.

Knowledge-based Expert System in Food Industry

The knowledge-based system is a computer program that utilizes knowledge from different sources, information, and data to solve complicated problems. It can be classified into three categories which are expert systems, knowledge-based artificial intelligence, and knowledge-based engineering. The breakdown of the knowledge-based system is presented in Fig.  1 . The knowledge-based expert system which is widely used in the industries is a decisive and collective computer system that is able to imitate the decision-making ability of human expert [ 40 ]. It is a type of knowledge-based system that is known as among the first successful AI models. This system depends on experts for solving the complicated issues in a particular domain. It has two sub-systems, which are knowledge base and inference engine. The facts about the world are stored in the knowledge base, and the inference engine represents the rules and conditions regarding the world which are usually expressed in terms of the IF–THEN rules [ 41 ]. Normally, it is able to resolve complicated issues by the aid of a human expert. This system is based on the knowledge from the experts. The main components of the expert system (ES) are human expert, knowledge engineer, knowledge base, inference engine, user interface, and the user. The flow of the expert system is shown in Fig.  2 .

Knowledge-based system

Expert system

The food industry has been utilizing ES for various objectives as this system is proven to be useful especially in the decision-making process. The knowledge-based expert system has been applied in white winemaking during the fermentation process for the supervision, intelligent control, and data recovery [ 42 ]. Apart from that, a web-based application was developed by implementing the ES to calculate the nutritional value of the food for the users, and the development of ES was able to help the SMIs in obtaining the details required for the qualification in obtaining the food production certificates [ 43 ]. Food safety is very important in the food industry,thus, the application of ES that is linked closely to food safety has been used extensively ranging from process design, safety management, quality of food, and risk assessment [ 44 ]. Furthermore, a prototype information technology tool and guidelines with corrective actions that considered ES in the model were developed for the food industry where few essential factors such as food safety, nutrition, quality, and cost were studied [ 45 ]. In addition, a digital learning tool, namely, MESTRAL, was developed to assist people in food processing by using models developed from research in food science and technology and simulators. This tool is based on the knowledge engineering and reflected real applications which can be mapped with the system scale and knowledge frameworks [ 46 ]. A comprehensive review was conducted by Leo Kumar on the application of the knowledge-based expert system in manufacturing planning. The paper has also discussed the utilization of ES in decision making in three wide areas which are the process planning activities, diverse applications, and manufacturing planning [ 41 ]. Moreover, Table 1 gathers some of the recent application of ES in the food industry ranging from the raw material to the final production as well as the food safety.

Fuzzy Logic Technique in the Food Industry

Fuzzy logic (FL) was first introduced by Zadeh in 1965 based on the impeccable capability of human intellect in decision making and unraveling the imprecise, uncertain, and ambiguous data while solving problems [ 47 ,  48 ]. The fuzzy set theory is recognized in such a manner that an element belongs to a fuzzy set with a certain degree of membership which has a real number in the interval [0, 1] [ 49 ]. FL models consist of several steps which are fuzzification, inference system, and defuzzification process [ 50 ,  51 ]. Fuzzification is a process where the crisp value is converted into a degree of membership and yields the fuzzy input sets. The corresponding degree in the membership functions is normally between 0 and 1. [ 52 ]. There are a variety of membership functions to choose from, whereby the commonly used ones are triangular, Z-shaped, S-shaped, trapezoidal, and Gaussian-shaped [ 52 ]. The inference system is where the fuzzy input is being translated to get output by using the fuzzy rules. The fuzzy rules are known as IF–THEN rules where it is written such IF premise, THEN consequent whereby the IF comprises input parameters and THEN is the output parameters [ 53 ]. The inference system consists of the style which is either the Mamdani or Takagi–Sugeno Kang (TSK). Defuzzification is the ultimate phase in the fuzzy logic model where the crisp values are obtained [ 54 ]. There are different methods of defuzzification which are center of gravity, mean of maximum, smallest of maximum, largest of maximum, center of maximum, and centroid of area [ 55 ].

FL has been long utilized in the industry due to its simplicity and ability to solve problems in a fast and accurate manner. FL has been employed in the food industry in food modeling, control, and classification and in addressing food-related problems by managing human reasoning in linguistic terms [ 56 ]. The food manufacturing system has improved by the implementation of the fuzzy logic where about 7% of electricity losses has been reduced compared to the conventional regulation method [ 57 ]. Sensory evaluation of the food is also one of the most common parts where FL plays an important role. Furthermore, a quicker solution to problems can be performed by using a system involving fuzzy rules [ 58 ]. Table 2 shows previous applications of FL in the food industry and their attributes. From a previous study, FL has been proven to successfully maintain the quality of the foods, and it acts as a prediction tool and control system for food production processes.

ANN Technique in the Food Industry

ANN is another AI element, which is also commonly applied in the food industry. ANN is designed to mimic the human brain and be able to gain knowledge through learning and the inter-neuro connections which are known as synaptic weights [ 59 ,  60 ]. Gandhi and coworkers have stated that the configuration of ANN is designed in such a way that it will accommodate certain application such as data classification or pattern recognition [ 61 ]. According to Gonzalez-Fernandez, ANN is applicable to a different kind of problems and situations, adaptable, and flexible. In addition, Gonzalez et al. (2019) have also stated that ANN is suitable to model most non-linear systems and is adaptable to new situations even though adjustments are needed. Moreover, the most outstanding features of ANN is its non-linear regression [ 62 ]. There are several types of ANN including feedforward neural network, radial basis function neural network, Kohonen self-organizing neural network, recurrent neural network, convolutional neural network, and modular neural network [ 63 ]. Multilayer perceptron (MLP), radial basis function networks (RBFNN), and Kohonen self-organizing algorithms are the most effective types of NN when it comes to solving real problems [ 61 ]. The most common network that is used for prediction and pattern recognition is the multilayer perceptron [ 18 , 64 ,  65 ]. Besides that, ANN learning could be classified into supervised and unsupervised depending on the learning techniques [ 17 ]. In general, the structure of ANN consisted of an input layer, hidden layer, and output layer, either single or many layers [ 66 – 68 ]. The architecture comprises activation functions, namely, the feed-forward or feedback [ 69 ]. The backpropagation learning algorithm is normally used as it is able to minimize the prediction error by feeding it back as an input until the minimum acceptable error is obtained [ 18 ]. An additional input known as bias is added to neurons which allows a portrayal of phenomena having thresholds [ 70 , 71 ]. In ANN, the dataset is normally associated with a learning algorithm which trained the network and could be categorized into three groups specifically supervised, unsupervised, and reinforcement learning [ 72 ]. Then, the data will undergo training and testing for analyzing the outputs. The general structure for the ANN is shown in Fig.  3 . The output data can be calculated by using the equation shown based on Fig.  4 .

ANN structure in general

General calculation in ANN

Previous studies have highlighted the utilization of ANN in numerous tasks within the food industry. This includes the assessment and classification of the samples, complex calculation such as heat and mass transfer, and analysis of the existing data for control purposes as well as for prediction purposes which are listed in Table 3 . All applications have shown satisfactory performances based on the R 2 values, showing that ANN can provide results in an accurate and reliable manner.

Machine Learning Techniques

Machine learning (ML) is known to be the subset of AI [ 73 ,  74 ]. It is a computer algorithm that advances automatically with experiences. ML can be classified into three broad categories which are supervised learning, unsupervised learning, and reinforcement learning [ 11 ,  75 ]. Supervised learning aims to predict the desired target or output by applying the given set of inputs [ 76 ]. On the other hand, unsupervised learning does not have any outputs to be predicted and this method is utilized to classify the given data and determine the naturally occurring patterns [ 77 ]. Reinforcement learning is when there is an interaction between the program and the environment in reaching certain goals [ 78 ]. Among the known models in machine learning are ANN, decision trees (DT), support vector machines (SVM), regression analysis, Bayesian networks, genetic algorithm, kernel machines, and federated learning [ 76 ,  79 ]. ML has been commonly used for handling complex tasks and huge amount of data as well as variety of variables where no pre-formula or existing formula is available for the problem. Other than that, ML models have the additional ability to learn from examples instead of being programmed with rules [ 80 ].

Among the ML methods that are used in the food industry include ordinary least square regression (OLS-R), stepwise linear regression (SL-R), principal component regression (PC-R), partial least square regression (PLS-R), support vector regression (SVM-R), boosted logistic regression (BLR), random forest regression (RF-R), and k-nearest neighbors’ regression (kNN-R) [ 81 ]. Studies showed that the usage of ML has helped in reducing the sensory evaluation cost, in decision making, and in enhancing business strategies so as to cater users’ need [ 82 ]. Long short-term memory (LSTM) which is an artificial recurrent neural network has been employed in the food industry as pH detection in the cheese fermentation process [ 83 ]. On the other hand, GA has been utilized for finding the optimum parameters in food whereas NN has been occupied to predict the final fouling rate in food processing [ 84 ]. ML has shown to be advantageous in predicting the food insecurity in the UK [ 85 ]. Apart from that, ML has also proven to have predicted the trend of sales in the food industry [ 86 ] In addition to that, ML was also able to predict the food waste generated and give an insight to the production system [ 87 ]. Major applications of ML in the food industry and its positive highlights are briefly emphasized in Table 4 .

Adaptive Neuro Fuzzy Inference System (ANFIS) Techniques

ANFIS is a type of AI where FL and ANN are combined in such a way that it integrates the human-like reasoning style of the FL system with the computational and learning capabilities of ANN [ 56 ]. In ANFIS, the learning procedure is transferred from the neural network into the FL system where a set of fuzzy rules with suitable membership functions from the data obtained is developed [ 88 ]. Mamat et al. [ 89 ] stated that uncertainty data could be processed and gain higher accuracy when ANFIS is applied [ 89 ]. Besides, ANFIS is also known as a fast and robust method in solving problems [ 90 ]. Not only that, Sharma et al. [ 91 ] also claimed that ANFIS has a higher performance compared to other models such as ANN and multiple regression models in their study [ 91 ]. ANFIS is a fuzzy reasoning system and combination of the parameters trained by ANN-based algorithms. The fuzzy inference system that is normally used is Takagi Sugeno Kang in the ANFIS model with the feedforward neural network consisting of the learning algorithms [ 92 ]. The structure of ANFIS is made up of five layers which are fuzzy layer, product layer, normalized layer, defuzzification layer, and total output layer [ 93 ,  31 , 32 ]. The backpropagation algorithm has been normally applied in the model in order to avoid over-fitting from occurring [ 92 ]. A high correlation value ( R 2 ) indicates that the developed model has high accuracy and is suitable for industrial applications. The general structure of the ANFIS model is illustrated in Fig.  5 .

General structure of ANFIS

The first layer in ANFIS has nodes that are adjustable, and it is called as the premise parameters [ 56 ]. The second layer in ANFIS has fixed nodes, and the output is the product of all incoming signals. Every output node represents the firing strength of the rule. The third layer consists of fixed node labeled as N. The outputs of the third layer are called normalized firing strengths. Every node in the fourth layer is an adaptive node with a node function, and the parameters in this layer are called as the subsequent parameters [ 56 ]. The final layer in the ANFIS layer has a fixed single node which calculates the overall output as the summation of all the incoming signals. The calculation involved in each layer is shown below. The output of the ith model in layer 1 is denoted as 0 1 , i.

Layer 1: \({O}_{1,i}= {\mu }_{Ai}\left(x\right), for i=\mathrm{1,2}\) atau \({O}_{1,i}= {\mu }_{Bi-2}\left(y\right), for i=\mathrm{3,4}\) .

Layer 2: \({O}_{2,i}= {w}_{i}={\mu }_{Ai}\left(x\right){\mu }_{Bi}\left(y\right), for i=\mathrm{1,2}\) .

Layer 3: \({O}_{3,i}=\overline{w }= \frac{{w}_{i}}{{w}_{1}+{w}_{2}}, i=\mathrm{1,2}\) .

Layer 4: \({O}_{4,i}= \overline{w}{f }_{i}={\overline{w} }_{i}({p}_{i}x+{q}_{i}y+{r}_{i}\) ); \({w}_{i}\) is the normalized firing strength from layer 3 and.

{ \({p}_{i},{q}_{i},{r}_{i}\) } is the parameter set of this node.

Layer 5: \({O}_{\mathrm{5,1}}=\sum_{i}{\overline{w} }_{i}{f}_{i}= \frac{\sum_{i}{w}_{i}{f}_{i}}{\sum_{i}{w}_{i}}\) .

The ANFIS model is attractive enough that it could solve problems related to the food industry, which are complicated, practical, and barely solved by other methods and has been widely used in the food industry for prediction and classification purposes. ANFIS has been applied in various food processing involving recent technology which comprised five main categories which are food property prediction, drying of food, thermal process modeling, microbial growth, and quality control of food as well as food rheology [ 56 ]. The utilization of ANFIS in the food industry has been commenced years ago, and Table 5 describes those applications.

Integrating AI with External Sensors for Real-time Detection in Food Industry

FL or ANN is often integrated with several sensors for real-time detection such as electronic nose (E-nose), electronic tongue (E-tongue), machine learning (ML), computer vision system (CVS), and near infrared spectroscopy (NIRS) for real-time detection and to obtain higher accuracy results in a shorter time. These detectors have also combined their elements together for enhancing their accuracy and targeted results. The integration of these sensors with the artificial intelligence methods has been shown quite a number in food industries over the past few years.

Electronic nose also known as E-nose is an instrument created to sense odors or flavors in analogy to the human nose. It consists of an array of electronic chemical sensors where it is able to recognize both simple and complex odors [ 94 ]. E-nose has been used in gas sensing where the analysis of each component or mixture of gases/vapors is required. Besides, it plays an important role in the food industry for controlling the quality of the products. Due to its ability to detect complex odors, it has been employed as an environment protection tool and detection of explosives materials [ 95 ]. An array of non-specific gas sensors is known to be the main hardware component of E-nose where the sensors will interact with a variety of chemicals with differing strengths. It then stimulates the sensors in the array where characteristic response is extracted known as a fingerprint [ 94 ]. The main software component of E-nose is its feature extraction and pattern recognition algorithms where the response is processed, important details are elicited and then chosen. Thus, the software component of the E-nose is greatly important to stimulate its performance. In general, E-nose is divided into three main parts, namely, sample delivery system, a detection system, and a computing system. ANN, FL, and pattern recognitions are the examples of the methodology employed in E-nose [ 96 ]. The general system of E-nose is shown in Fig.  6 .

E-nose system

E-nose has been widely used to aid in both quality control and assurance in the food industries. Wines, grains, cooking oils, eggs, dairy products, meat and dairy products, meat, fish products, fresh-cut and processed vegetables, tea, coffee, and juices have successfully applied e-nose for sampling classification, detection, and quality control. E-nose has successfully classified samples with different molecular compounds [ 97 ]. Besides, Sanaeifar et al. have reviewed and confirmed that e-nose was able to detect defects and contamination in foodstuffs [ 98 ]. Classification and differentiation of different fruits have also determined by using e-nose [ 99 ]. A review has been conducted on the application of the E-nose for monitoring the authenticity of food [ 100 ]. Adding to this, Mohamed et al. have carried out a comprehensive review on the classification of food freshness by using e-nose integrated with the FL and ANN method [ 101 ]. Recent application of e-nose with computing methods involving AI in food industries is shown in Table 6 .

Electronic tongue (E-tongue) is an instrument that is able to determine and analyze taste. Several low-selective sensors are available in E-tongue which is also known as “a multisensory system,” and advanced mathematical technique is being used to process the signal based on pattern recognition (PARC) and multivariate data analysis [ 102 ]. For example, different types of chemical substances in the liquid phase samples can be segregated using E-tongue. About seven sensors of electronic instruments are equipped in E-tongue, which enabled it to identify the organic and inorganic compounds. A unique fingerprint is formed from the combination of all sensors that has a spectrum of reactions that differ from one another. The statistical software of E-tongue enables the recognition and the perception of the taste. E-tongue comprises three elements specifically the sample-dispensing chamber or automatic sample dispenser, an array of sensors of different selectivity, and image recognition system for data processing (Ekezie, 2015). Samples in liquid forms could be analyzed directly without any preparation while the samples in solid forms have to undergo preliminary dissolution before measurement is carried out. The process of E-tongue system is shown in Fig.  7 below. The ability to sense any taste like a human olfactory system makes it one of the important devices in the food industry, especially for quality control and assurance of food and beverages [ 103 ]. In addition, E-tongue has been used to identify the aging of flavor in beverages [ 104 ], identify the umami taste in the mushrooms [ 105 ], and assess the bitterness of drinks or dissolved compounds [ 102 ]. Jiang et al. performed a summarized review on the application of e-nose in the sensory and safety index detection of foods [ 106 ]. Moreover, the demand of E-tongue in the food industry market has risen due to the awareness on delivering safe and higher-quality products. The details of recent applications of E-tongue in the food industry are shown in Table 7 .

E-tongue system

The computer vision system (CVS) is a branch of AI that combines the image processing and pattern recognition techniques. It is a non-destructive method that allows the examination and extraction of image’s features to facilitate and design the classification pattern [ 107 ]. It is also recognized as a useful tool in extracting the external feature measurement such as the size, shape, color, and defects. In general, it comprised a digital camera, a lighting system, and a software to process the images and carry out the analysis [ 108 ]. The system can be divided into two types which are 2D and 3D versions. Its usage is not restricted to various applications in food industries such as evaluating the stages of ripeness in apples [ 107 ], predicting the color attributes of the pork loin [ 109 ], detecting the roasting degree of the coffee [ 110 ], evaluating the quality of table grapes [ 111 ], and detecting the defects in the pork [ 112 ]. The combination of CVS with soft computing techniques has been said as a valuable and important tool in the food industry. This is because the combination of these systems offers good advantages such as an accurate prediction in a fast manner can be achieved. Table 8 shows the combination of CVS and soft computing that has been used in the food industry. Figure  8 shows the working principle of CVS. An example on the utilization of CVS for the quality control is shown in Fig.  9 [ 113 ].

Working Principle of CVS

CVS-based quality control process

Near infrared spectroscopy (NIRS) is another technique in the food industry as there is no usage of chemicals and results can be obtained accurately as well as precisely within minutes or even continuously [ 114 ]. In addition, it is known to be non-destructive, cost effective, quick, and straightforward which makes it a good alternative for the traditional techniques which are expensive and labor intensive and consumes a lot of time [ 115 ]. The chemical-free method by NIRS makes it suitable to be used as a sustainable alternative since it will not endanger the environment or the human health. It has a wide range of quantitative and qualitative analysis of gases, materials, slurries, powders, and solid materials. Furthermore, samples are not required to be grounded when light passes through it and certain features or characteristics that are unique to the class of the sample are revealed by the spectra of the light. Complex physical and chemical information on the vibrational of molecular bonds such as C–H, N–H, and O–H groups and N–O, C–N, C–O, and C–C groups in organic materials can be provided by the spectra which can be recorded in reflection, interactance, or in transmission modes [ 114 ].

The basic working principle for NIRS is shown in Fig.  10 . Recently, NIRS has become an interest in food industries to inspect food quality, controlling the objective of the study and evaluating the safety of the food [ 114 ,  116 – 119 ]. Several researchers have applied the NIRS in food to obtain its properties for multiple reasons including determining the fatty acid profile of the milk as well as fat groups in goat milk [ 120 ]. Apart from that, it is able to aid in the prediction of salted meat composition at different temperatures [ 121 ] and in the prediction of sodium contents in processed meat products [ 122 ]. The detection and grading of the wooden breast syndrome in chicken fillet in the process line was also able to be performed by using the NIRS technique [ 123 ]. Not only that, it is proven to be efficient in determining the maturity of the avocado based on their oil content [ 124 ], predicting the acrylamide content in French-fried potato and in the potato flour model system [ 125 ], and determining the composition of fatty acid in lamb [ 126 ]. There has been a review conducted on the application of the ANN combined with the near-infrared spectroscopy for the detection and authenticity of the food [ 127 ]. The ability of the NIRS system in detecting the physical and chemical properties coupled with soft computing techniques such as ANN, FL, and ML allows the classification and prediction of the samples to be performed rapidly and accurately. Table 9 shows the application of NIRS coupled with AI techniques in the food industry.

Basic working principle of CVS

Summary on the Application of AI in the Food Industry

From the review so far, it can be shown that AI has been used for various reasons in food industries such as for detection, safety, prediction, control tool, quality analysis, and classification purposes. Ranking of sensory attributes in the foods can be done easily by using the FL model. Not only that, fuzzy logic can be used for classification, control, and non-linear food modeling in the food industry. ES is widely used in the food industry for decision-making process. On the other hand, ANN model is applied widely in the food industry for prediction, classification, and control task as well as for food processing and technology. The supervised ANN method has the ability to learn from examples which allows for the prediction process to be done accurately. Meanwhile, the unsupervised method of ANN is found to be more common for the classification task. Another method that has been utilized for the prediction and classification of the food samples is by using the machine learning (ML) method. ML can be used in solving complicated tasks which involves a huge amount of data and variables but does not have pre-existing equations or formula. This method is known to be useful when the rules are too complex and constantly changing or when the data keep changing and require adaptation. Furthermore, the adaptive neuro fuzzy inference system (ANFIS) is another hybrid AI method that can be used to solve sophisticated and practical problems in the food industry. However, decent data are required for the model to learn in order to perform well. In addition to that, this model is useful for solving analytical mathematical models in the food industry such as studies involving mass and heat transfer coefficients. ANFIS is recommended to be used when complex systems where time-varying processes or complex functional relationships and multivariable are involved. Apart from that, it can be used in descriptive sensory evaluation.

These AI algorithms can be combined with other sensors such as the electronic nose, electronic tongue, computer vision system, and near infrared spectroscopy to glean the data from the samples. Both the E-nose and E-tongue have shown to enhance the quality characteristics in comparison to the traditional detection approach [ 128 ]. E-nose can be used to sense the odors or gases while the E-tongue can be applied for the identification of the organic and inorganic compounds. Studies involving the examination and drawing out the features of the samples like shape, color, defects, and size can be carried out by using the CVS sensors. NIRS can be utilized to determine the properties or contents in the samples. The data obtained from these sensors is then merged with the AI algorithms and utilizing their computing strengths to accomplish the desired studies.

Advantages and Disadvantages of AI

AI has been used widely in the industry as it offers a lot of advantages compared to the traditional method. All the algorithms are known to be accurate and reliable, but careful selection should be made by considering the advantages and limitations of the algorithms. The different algorithms have their own strengths and weakness, hence choosing them for a particular application in the food industry needs to be looked on a case-to-case basis. The guideline to choose the most appropriate method is given in the next section. The benefits and constraints that each of the algorithm exhibits are explained briefly in Table 10 .

Guidelines on Choosing the Appropriate AI Method

Selecting the appropriate algorithm is important when developing the AI model as it can aid the user to attain an accurate, rapid, and cost-saving results. Therefore, a guideline given in Fig.  3 is an important asset prior to achieving best performances in a case study. The primary step in the selection process is that users should define and finalize the objective of using AI in their research or implementation. Prediction, classification, quality control, detection of adulterants, and estimation are among the common objectives of AI applications in the food industries. Next, decision should be made whether sensors such as E-tongue, E-nose, CVS, and NIRS are required to collect the sampling data or not for collecting the data from the samples. Normally, integration with those sensors is conducted to obtain the parameters and characteristics of the samples to be included in the AI algorithms for sample testing purposes. Upon deciding the necessity of the sensors, users should compare and choose the fitting algorithm with respect to their study. Among the most common AI algorithms that have been employed include the FL, ANN, ANFIS, and ML methods. ANFIS has shown to have a higher accuracy, but the complexity of the model makes it less favorable compared to the other algorithms. It is advisable for the users to determine the complexity of the research in selecting the most appropriate algorithm for their studies. Once the selection of the algorithm has been confirmed, the data available are integrated with the AI algorithms. Finally, the testing and validation based on R 2 and MSE are done to analyze the performance of the established model. The AI model has been created successfully once the validation is accepted; otherwise, users should return to the previous step and reselect the algorithm. Figure 11  shows the guideline in choosing and development of the AI model in food industry application.

Flowchart for developing AI model

Trends on the Application of AI in the Food Industry in the Future

The overall trend on the application of AI in the food industry is shown in Fig. 12 . From the studies within the past few years, the usage of the AI methods has been observed to increase from 2015 to 2020 and is predicted to rise for the next 10 years based on the current trends. Among the rising factors for the application of AI in the food industry is the introduction of Industrial Revolution 4.0 (IR 4.0). The merging of technologies or intelligent systems into conventional industry is what is known as IR 4.0 and can also be called smart factory [ 129 ,  130 ]. AI which is categorized under the IR 4.0 technologies focuses on the development of intelligent machines that functions like the humans [ 131 ]. IR 4.0 makes a great impact in the product recalls due to the inspections or complains in the food industries. The implementation of the AI integrated in the sensors able to detect the errors during the manufacturing process and rectify the problems efficiently. Apart from that, IR 4.0 also plays a big role in the human behavior as consumers in the twenty-first century often discover information regarding the foods in the internet. The rising concerns on the food quality allow more usage of AI as they are able to enhance the quality of the food and aids during the production process. The highest amount of application of AI in the food industry was seen in the year 2020 as more researchers are carrying out studies using the AI method, and it is believed to continue rising for the upcoming years due to increasing in food demand and the concern on the safety of the foods which are being produced.

Application of AI in the food industry

The comparison between the AI integration with and without sensors for real-time monitoring in the food industry is displayed in Fig.  13 . Integration with external sensors has a higher percentage compared to those without the integration of the sensors in the food industries. The purpose of external sensors was to obtain the data from the samples which are then employed into the AI algorithms to carry out various tasks such as classification, prediction, quality control, and others that have been stated earlier. However, the data collection for the year 2017 showed that the percentage for the AI without the external sensors is greater than that with integration with the sensors. This is due to the high amount of research which was conducted without using the external sensors which are listed in this paper. Based on the evaluation carried out during this study, it was found that a high amount of research was done on the integration of CVS sensors with the AI methods. It is explainable as CVS sensors are able to provide important parameters such as the shape, size, colors, and defects which are essential for the quality control in the food industry. However, the integration of the system is mainly dependent on the objectives of the researcher and the industrial players and the availability of the data.

Comparison between integration of AI for real-time monitoring in the food industry

In short, as the AI world is heading towards 2.0 [ 132 ], it can be predicted that the rise in the usage of AI in the food industry is definite and inevitable because of the advantages that they can offer such as saving in terms of time, money, and energy as well as the accuracy in predicting the main factors which are affecting the food industries. Apart from that, in the recent pandemic situation due to the Covid-19 virus, it is predicted that more companies will opt for the usage of AI in their industries to cut down the costs and boost the performance of their company. There have been reports by some of the SMEs that their earnings have dropped and some SMEs have claimed that they could only survive for about 1 to 3 months. The high demand of food and the tight standard operating procedure in the companies during the pandemic situation will encourage the industry players to find an alternative to their problems and AI will be one of them to ensure a smooth operation.

Conclusion and Future Outlook

In conclusion, AI has been playing a major role in the food industry for various intents such as for modeling, prediction, control tool, food drying, sensory evaluation, quality control, and solving complex problems in the food processing. Apart from that, AI is able to enhance the business strategies due to its ability in conducting the sales prediction and allowing the yield increment. AI is recognized widely due to its simplicity, accuracy, and cost-saving method in the food industry. The applications of AI, its advantages, and limitations as well as the integration of the algorithms with different sensors such as E-nose and E-tongue in the food industry are critically summarized. Moreover, a guideline has been proposed as a step-by-step procedure in developing the appropriate algorithm prior to using the AI model in the food industry–related field, all of which will aid and encourage researchers and industrial players to venture into the current technology that has been proven to provide better outcome.

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The authors were supported by the Universiti Kebangsaan Malaysia under grant GUP-2019–012.

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Nidhi Rajesh Mavani, Jarinah Mohd Ali, Suhaili Othman & Norliza Abd Rahman

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Mavani, N.R., Ali, J.M., Othman, S. et al. Application of Artificial Intelligence in Food Industry—a Guideline. Food Eng Rev 14 , 134–175 (2022). https://doi.org/10.1007/s12393-021-09290-z

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Sustainable Supply Chain Management in the Food Industry: A Conceptual Model from a Literature Review and a Case Study

Associated data.

The new data that were created and analyzed in this study are the ones presented in the article. Interview or field notes data sharing is not applicable to this article.

The purpose of this study is twofold: firstly, to provide a literature review of sustainable supply chain management (SSCM) critical factors, practices and performance; and secondly, to develop a comprehensive and testable model of SSCM in the food industry. The research conducted comprises a literature review and a case study. The literature review findings propose a theoretical framework linking SSCM critical factors, practices and performance. The case study comprises two sustainability leaders in the Greek food supply chain in order to investigate the three SSCM constructs. A new set of pioneering SSCM practices in the Greek food industry is identified, including daily conversation, local sourcing and HR investments. The end result of this research proposes a testable model that sheds light on SSCM in the food industry and is based on a set of propositions.

1. Introduction

Over the past decades, sustainable supply chain management (SSCM) has attracted much attention from academics and practitioners [ 1 , 2 ]. Globalisation allowed processes to be dispersed around the world, linking all supply chain members, from suppliers to end customers, through information sharing and material and capital flows [ 2 ]. As a result, pressures from stakeholders, such as regulatory bodies, non-governmental organisations (NGOs), community organizations, suppliers, customers and global competition, have prompted companies to reconsider the balance of environmental, social and economic issues in their supply chains [ 3 ] and adopt sustainable supply chain management practices. SSCM is defined as “the management of material, information and capital flows as well as cooperation among companies along the supply chain while taking goals from all three dimensions of sustainable development, i.e., economic, environmental and social, into account which are derived from customer and stakeholder requirements” [ 2 ] (p. 1700).

As in all business operations, SSCM tries to achieve clearly defined performance goals [ 4 ]. However, this is not an easy task due to the complexity of supply chains, where individual members have different and often conflicting goals from other members of the chain and hence different performance measures. Different measures are not always seen as positive regarding the entire chain’s performance, because a single company’s outcomes may be harmful for other supply chain members. Hence, the performance of the entire chain can only be improved if the supply chain is conceptualized as a whole, outside the boundaries of the firm level [ 5 ].

SSCM practices such as environmental purchasing and sustainable packaging often have positive outcomes regarding supply chain sustainability performance [ 6 ]. The development of SSCM practices can either be enabled or inhibited by various contingent factors. A variety of industries face specific enabling or inhibiting factors from different points of view based on their size, culture, location and supply chain partners. Many researchers have studied SSCM in several sectors such as manufacturing [ 2 , 7 ], the automotive industry [ 8 ], oil and gas [ 9 ], energy [ 10 ] and the food industry [ 11 ]. The food industry is one of the most important sectors that faces significant environmental, economic, social and political challenges. This is due to the focus of public attention on food safety, production practices, environmental issues such as deforestation, climate change and energy consumption and social issues such as fair wages and population growth [ 11 , 12 ]. Furthermore, globalization, technological advances, the use of agricultural chemicals and improved transportation have simultaneously raised concerns regarding the sustainability of food supply chains [ 13 , 14 ], since “changes at one stage in a supply chain will have knock-on effects on other stages in the chain” [ 14 ] (p. 97).

Other critical issues are related to the measurement of supply chain impacts, to supply chain collaboration and networking, to stakeholder engagement, to sustainable development goals, etc. [ 15 ]. These challenges confirm the differentiability of food supply chains, which lies upon variability and risk factors due to the product-specific characteristics such as perishability, seasonality in production, transportation and storage conditions [ 16 ]. In addition, customers and firms have raised their concerns regarding the origin of products, food safety, quality and sustainable production [ 17 ], including animal welfare and environmental pressure [ 16 ].

Numerous studies have investigated the relationship between SSCM practices and sustainability performance. However, limited work has been conducted on the empirical investigation of industry and location-specific SSCM critical factors and practices and their relationship to sustainability performance [ 11 , 18 , 19 , 20 , 21 ]. The food industry is characterised by enhanced supply chain relationships that aim at achieving high sustainability performance [ 11 ]. Earlier findings from ref. [ 6 ] demonstrate that environmentally friendly purchasing and sustainable packaging result in improved economic and social performance. Direct and indirect impacts between the dimensions of sustainability performance are also observed in the literature. A positive relationship is found between corporate social performance and financial performance [ 22 ]. In the wine industry, ref. [ 23 ] found that employee practices related to social sustainability result in reduced costs; ref. [ 24 ] found that environmental practices have positive environmental performance outcomes and indirect impacts on cost performance based on quality improvements. The authors of ref. [ 25 ] suggest an alignment between goals that lead to improved environmental and financial performance. On the other hand, ref. [ 24 ] highlights “the complexity of sustainability impacts on performance and suggest that performance benefits from sustainability programs may be difficult to recognize” [ 24 ] (p. 38).

With the above in mind, the aim of this study is to gain insight into the SSCM critical factors and practices that are implemented in the food industry and their possible relationship to sustainability performance. To support the purpose of this research, two methods were used. A literature review of the key SSCM topics and a case study to demonstrate the experience of two leaders in SSCM. The aim of this research will be achieved by addressing the following research questions (RQ):

RQ1: What are the factors that influence the adoption of SSCM practices in the food industry?

RQ2: Which practices do companies in the food industry adopt to develop SSCM?

RQ3: What measures can be used to measure SSCM performance in the food industry?

The rest of the paper is organized as follows. The next section presents the literature review and the case study methods. The results of the literature review and the case study are presented and discussed in conjunction with previous research in Section 3 . Finally, the conclusions are drawn in Section 4 , including the study limitations as well as future research opportunities.

2. Materials and Methods

The research methodology that was applied in this study is based on the following steps [ 15 ]: (i) a literature review; (ii) identification of the gaps; (iii) concepts synthesis; and (iv) a case study.

2.1. Literature Review Method

Because the identification and conceptualisation of SSCM is still unclear, a literature review was conducted on the key sustainable supply chain management topics, such as critical factors for implementation, practices and performance. Despite the fact that other reviews on the SSCM are already published, this review is required in order provide an up-to-date report and understanding of the current SSCM research. The search for related scientific articles was based on keywords and authors’ names, in major bibliographical databases and publishers such as Scopus, Elsevier, Emerald, Springer, Wiley, Taylor & Francis, Springer, Sage Publications and Inderscience, over a twenty-year period since 2000. The keywords search included “sustainable supply chain management”, “drivers”, “barriers”, “enablers”, “motivators”, “critical factors”, “sustainable supply chain management practices”, “sustainability performance” and “food industry”. The authors search included Seuring S., Beske P., Gualandirs J., Govindan, K, Pagell M., etc., since these authors have repeatedly focused their research on SSCM topics [ 1 ]. A secondary search was also carried out using the cited references. Only papers in peer-reviewed English scientific journals are reviewed. This research includes articles with a focus on the food industry as a field of application but is not limited to that. Articles from other sectors were also included in the study.

The measures identified by the comprehensive literature review were named and grouped based on the affinity method, which is utilized to organize into categories common themes from a large amount of information [ 26 ]. In addition to the affinity method, the naming and grouping of the constructs were based on interviews of five professionals of the food industry and five academics.

2.2. Case Study Method

Taking into account that the analysis of a supply chain as a whole is a complex and difficult task and in order to explore the SSCM critical factors, practices and performance in the food industry, a case study was selected as the most appropriate research method [ 27 , 28 ]. This study investigates a sustainable supply chain in order to capture the critical factors of SSCM, the SSCM practices adopted and their influence on sustainability performance. The research has been carried out in a supply chain that is comprised of two SSCM leaders that operate in Greece ( Table 1 ). This is particularly useful, because it offers empirical contributions within the Greek-business context, where SSCM literature is limited. The names of the companies were not disclosed in order to protect confidentiality and encourage the openness of responses. The unit of analysis in this study is the food supply chain. The study investigates the particular food supply chain, comprised of two companies, and the findings will concern the supply chain as a whole.

Sample characteristics.

The authors of ref. [ 29 ] propose a five-stage process for case studies that is used for structuring this research. Figure 1 depicts the various research steps.

The five stages of the research process model.

• The first step is related to the research objective. This research uses a single case study to investigate the critical factors that influence companies in the food industry to implement SSCM practices, which are these practices and how do they influence sustainability performance.
• The second step is related to the research instrument development. A single case research design is used to guide this study and provide an in-depth understanding of a complex phenomenon, through the observation of actual practices in real-world settings, without any kind of control or manipulation, considering both temporal and contextual dimensions [ 30 , 31 ]. Case studies provide researchers the opportunity to closely analyse the data within a specific context. In ref. [ 32 ] (p. 18), the authors define the case study research method “as an empirical inquiry that investigates a contemporary phenomenon within its real-life context; when the boundaries between phenomenon and context are not clearly evident; and in which multiple sources of evidence are used.” Furthermore, the detailed qualitative accounts often produced in case studies not only help to explore or describe the data in real-life environments but also help to explain the complexities of real-life situations, which may not be captured through experimental or survey research [ 33 ]. For the reasons referred to above, a single case study comprised by two leaders in the food industry was selected as the most appropriate research method for this study. The firms are both sustainability leaders in the Greek food industry and members of multinational groups. The companies were selected as they have received a series of recognitions regarding sustainability, such as Environmental Awards, Supply Chain Sustainability Awards, distinctions in CSR actions, etc. Furthermore, both companies play a crucial role in the Greek industry, society and economy. An interview protocol [ 27 ] was developed on the basis of the reviewed literature and closely following previous research on SSCM [ 34 , 35 ] (see Appendix A ). The authors of ref. [ 36 ] highlight that using existing questions enables the comparability of results. Furthermore, ref. [ 28 ] points out that using interview protocols assures the reliability of data. The interviews ranged from 70 to 90 min.
• (1) The CSR Manager of the SB company;
• (2) The Quality Manager of the SB company;
• (3) The Manager of the distribution centre of the SM company;
• (4) Two Logistics Project Managers of the SM company;
• (5) Three Retail Store Managers of the SM company.

Field notes were typed up during each interview. Repeated contacts by phone or e-mails were needed to confirm the chain of evidence. Except for the data drawn from interviews, the analysis of the sustainability reports in combination with the website information and other news were important secondary sources.

• 4. The fourth step refers to the data analysis. The data analysis was filtered and guided by the identified SSCM constructs.

The fifth step is related to assuring the quality of the research process: Multiple sources of data were collected, including archival data (financial reports, CSR reports, website material and company records), on-site observations and semi-structured interviews, in order to achieve data source triangulation and ensure construct validity ([ 37 ], p. 68; [ 28 ], p. 36). The internal validity of the case was assured by doing pattern matching with other studies identified in previous research [ 28 ]. Regarding the external validity, the case study was designed and conducted based on the gathering of as many data as possible in order to attain a deeper knowledge of the complex background of SSCM and to identify the more analytical and general theoretical implications [ 28 ].

3. Results and Discussion

This section begins with the literature review results, highlighting the concepts of the sustainable supply chain management and continues with the case study results of the food supply chain.

3.1. Literature Review Results

The results of the literature review are classified in three main SSCM content categories, namely, critical factors, practices and performance.

3.1.1. Critical Factors

In studying the literature, many terms are found to be used interchangeably by researchers. For example, the factor top management commitment is considered as enabler [ 35 , 38 ], driver [ 39 ], success factor [ 40 ], critical factor [ 41 ], enabling factor [ 42 ], reason [ 43 ], motivator [ 19 ] and firm-level strength [ 44 ]. In contrast, most researchers in the SSCM literature use the term barrier when describing factors that inhibit SSCM, such as the lack of top management commitment [ 1 , 35 , 39 ]. As observed, there are several terms to describe the same factor, indicating a lack of agreement on how these terms should be used in SSCM research. Furthermore, these factors are classified in more than one category, such as internal and external [ 35 , 45 , 46 ], regulatory, resource, market and social [ 47 ], stakeholder, process or product [ 48 ].

The identified factors are named critical factors, including enablers, drivers, success factors, motives as well as barriers and inhibiting factors. More specifically, in this study critical factors are defined as the factors that are responsible for enabling or inhibiting the successful implementation of SSCM. This is the rationale for grouping the enablers, drivers, success factors, motives, barriers and inhibiting factors in one group. This approach is also applied in other studies that investigate SSCM [ 41 ]. A total of 83 critical factors were identified in the literature from 34 papers. The critical factors are classified into three groups. The first group is related to firm-level critical factors (FLCF), the second to supply chain-level critical factors (SCLCF) and the third to external critical factors (ECF). All three groups of factors play a major role in the success or failure of the implementation of SSCM [ 1 ].

Firm-Level Critical Factors (FLCF)

Sustainable supply chain management scholars have asserted that firms should consider multiple factors that will enable or hinder the successful implementation of SSCM practices [ 1 ]. Several critical factors from various industries and countries have been identified in the literature [ 1 ]. Top management commitment and support is considered the most common FLCF [ 35 , 38 , 40 ]. In ref. [ 49 ], the authors have highlighted that top management is responsible for directing sustainability efforts; [ 50 ] also found that senior corporate management’s attitude can foster plant-level sustainability management. Indeed, the implementation of SSCM is an internal decision that has to be supported at the firm level [ 43 ]. From a supply chain-level perspective, ref. [ 39 ] have found that top management is a factor that drives purchasing and supply management sustainability initiatives. On the other hand, low or lack of top management commitment is considered by many researchers as a barrier for the successful implementation of SSCM [ 1 , 46 ]. In the food industry, ref. [ 34 ] have found that the most common critical factors for SSCM adoption are the operational cost reduction and market drivers, such as customer requirements, retailer pressure and brand image and corporate reputation. Meeting customer demands, expectations and requirements is one of the most cited critical factors for the implementation of SSCM [ 35 , 39 , 47 ]. It is widely accepted that customers are the stakeholder group that influences most a company’s performance by buying or rejecting a specific product [ 51 ]. For example, there are customers that desire to have environmentally friendly products and services and they are willing to pay more for their demand. If companies fail to meet this specific requirement, they may face customer boycotts [ 43 ]. In ref. [ 39 ], the authors identified knowledge and expertise regarding sustainability as a driving force for developing an organisation’s SSCM strategy, while ref. [ 47 ] highlighted knowledge as a critical intangible asset for SSCM implementation. Indeed, competences, knowledge and expertise are crucial factors for the successful or unsuccessful implementation of SSCM [ 39 , 41 , 43 , 47 ]. The recent study of ref. [ 18 ] has shown that companies that invest in human capital with professional expertise and capabilities on sustainability issues can enable the implementation of SSCM practices. In the same line, ref. [ 47 ] mentions that the lack of knowledge about sustainability issues hinders the development of SSCM. Training and education are other key firm-level critical factors that are closely related to sustainability performance [ 18 ]. Training and development about sustainability allow for sustainability improvements in job performance and helps companies minimise errors and waste [ 18 ]. A lack of training and education, on the other hand, hinders successful SSCM implementation [ 1 ]. In ref. [ 24 ], the authors found that despite the fact that social sustainability practices, including participation and training of employees, indirectly impact firm performance, they are positively related. More specifically, social sustainability practices are considered quality-enablers in the food sector [ 24 ]. Reputation critical factors are related to brand name and reputation, or minimization of the risk of negative publicity [ 47 ]. The authors of ref. [ 47 ] highlight that corporate reputation and image are positively related to eco-brand developments. Being proactive regarding sustainability issues can bring a good reputation and image and offer easier market access and develop a good network of suppliers and partners [ 52 ]. In ref. [ 53 ] (p. 325), the authors further explain that “organizations build a reputation of ‘good citizen’ by promoting environmental and social sustainability in their supply chain. This reputation improves legitimacy and access to key resources”. Firm-level critical factors related to financial issues include cost savings from operational and material efficiencies [ 47 ] and the increased resource utilization [ 39 ]. On the opposite side, companies that desire to adopt SSCM practices often struggle to overcome the high costs related to the upstream supply chain greening [ 47 ] or the development of supply chain infrastructure, systems and processes [ 19 ].

Supply Chain-Level Critical Factors (SCLCF)

Supply chain-level CFs are closely linked to firm-level CFs. The literature posits that firm level and supply chain-level alignment strongly affect their successful integration [ 54 ]. Information sharing has been identified as one of the most important enablers to adopt SSCM practices [ 38 , 40 , 41 ]. In ref. [ 18 ], the authors suggest that information sharing enables the development of new ideas regarding sustainability and enhances collaboration throughout the supply chain. In the food industry, information sharing among supply chain members is described as a novel form for traceability and it is linked to improved supply chain performance [ 25 ]. Ref. [ 34 ] mentions that product traceability is strongly related to social sustainability and ensures food safety. The limited or lack of information and transparency on sustainability related issues, on the other hand, has a negative impact on SSCM implementation [ 41 , 42 ]. Trustful relationships and commitment among supply chain partners is mentioned as a key factor for implementing SSCM in the food industry. This is due to the criticality of ingredient quality in the food production [ 41 ]. According to ref. [ 34 ], who investigated sustainability in the Italian meat supply chain, building trust amongst supply chain firms is a core component for implementing exceptional supply chain practices, such as supplier collaboration, for sustainability. On the contrary, ref. [ 1 ] highlights that poor supplier commitment is one of the most common inhibiting factors. In ref. [ 46 ], the authors found that the lack of trust and commitment between supply chain members is an important obstacle, especially when customers audit suppliers. Agreeing on a common SSCM strategy is another important supply chain critical factor. The authors of ref. [ 40 ] found that it is more likely for companies that signal sustainability initiatives to their supply chain partners and stakeholders to develop a common SSCM strategy with them. Developing a common SSCM strategy ensures that all supply chain partners pursue the same strategic goal [ 40 ]. Indeed, policy sharing, and the subsequent establishment of common goals, was found to be a key factor for the implementation of SSCM practices such as environmental collaboration [ 55 ]. Ref. [ 11 ] found that pro-activity is a key factor when pursuing an SSCM strategy in the food industry (e.g., organic food or fair trade) since new processes and technologies need to be established. The lack of agreement on an SSCM strategy hinders the adoption of SSCM. Another factor that significantly affects the adoption of SSCM practices is geographical distance. The findings of ref. [ 56 ] show that when geographical distance between suppliers increases, a negative impact is observed on data gathering, assessment and collaboration. More specifically, ref. [ 41 ] found that when visiting distant farms or manufacturing plants is required, significant travel effort and resources are needed and as a result it is more difficult to check the partners’ operations and processes. On the contrary, shorter supply chains often lead to the successful implementation of sustainability practices [ 57 ].

External Critical Factors

External CFs originate from a variety of stakeholders, such as government, customers, suppliers, media, non-governmental organizations (NGOs), etc. Two of the most common external critical factors for SSCM are the existence of regulatory frameworks [ 38 , 39 , 47 ] and the awareness of and compliance to government policy and legislations [ 18 , 35 , 39 , 58 , 59 ]. Pressure from governments in the form of legislation, such as energy and waste directives, international regulations such as the UN Declaration of Human rights and International Labour Organization conventions, or the EU’s Sustainable Consumption, Production and Sustainable Industrial Policy Action Plan are critical factors for the implementation of SSCM in the food industry [ 47 , 52 ]. Furthermore, pressure from investors [ 35 , 47 ] and interaction with NGOs and other external stakeholders [ 42 ] may exert pressure on companies to implement SSCM. Pressures from investors, such as increased investor appeal on sustainability criteria, are considered a driving force to initiate and maintain SSCM [ 35 , 47 ]. Food scares regarding pesticide residues, unhealthy ingredients, chemical residues, etc., result in cautious measures [ 47 ]. Other studies have identified competitor’s pressure as a market factor that may lead to the development of SSCM practices. Refs. [ 35 , 39 , 40 , 47 ] posit that the adoption of SSCM practices by competitors motivates companies to develop SSCM.

Additional SSCM critical factors are identified in the literature but are not included here, since the concentration in this paper is on those factors that are relevant for sustainable supply chain management in the food industry. A comprehensive list would have to include critical factors such as innovativeness, technology and equipment [ 18 ]; employee involvement and traditional accounting methods [ 35 ]; additional human resources [ 42 ]; personnel commitment [ 41 ]; Industry 4.0 solutions [ 60 , 61 , 62 ], including the Internet of Things (IoT), sustainability data and information [ 42 ]; and the supply chain cultural and language differences [ 41 ]; among others.

3.1.2. Practices

In ref. [ 63 ] (p. 620), supply chain management practice is defined as “a set of activities undertaken in an organization to promote effective management of its supply chain”. In combination with the definition of SSCM that has been provided in the introduction, SSCM practice is defined as a set of sustainability (i.e., economic, environmental and social) activities undertaken in an organization in cooperation with each stakeholders, to promote effective sustainability management of its supply chain. SSCM practices have their origins in green supply chain management (GSCM). Ref. [ 8 ] have examined the relationships between GSCM practices and organizational performance in the Chinese manufacturing and processing sectors. In their study they categorized GSCM practices into four groups: (1) Internal environmental management; (2) External GSCM practices; (3) Investment recovery; and (4) Eco-design. Their results have shown that GSCM practices tend to have a positive relationship with environmental and economic outcomes. The same authors three years later used internal environmental management, green purchasing, eco-design, cooperation with customers and investment recovery to represent GSCM practices in their empirical study [ 64 ]. Ref. [ 65 ] investigated the impact of GSCM practices on organizational performance in the electrical and electronic sector. Their results indicate that green procurement and green manufacturing practices have a positive influence on environmental and financial performance. The authors in ref. [ 66 ] identified 47 different logistics social responsibility (LSR) practices and developed a taxonomy of five categories including socially responsible purchasing, sustainable transportation, reverse logistics, sustainable packaging and sustainable warehousing. The authors in ref. [ 67 ] have empirically investigated the influence of environmental collaboration practices in the supply chain on environmental and manufacturing performance. In ref. [ 25 ], five bundles of SSCM practices were identified through case studies of ten exemplar firms: (1) commonalities, cognitions and orientations; (2) ensuring supplier continuity; (3) re-conceptualize the chain; (4) supply chain management practices including sourcing management, operations and investments in human capital; and (5) measurement. In their list of SSCM practices in the food industry, ref. [ 24 ] included both social and environmental issues. More specifically, they have identified four types of SSCM practices, namely, land management, recycling, facility conservation and social practices, and tested their relationships to environmental, quality and cost performance. Focusing on a more social perspective of supply chains, ref. [ 56 ] developed a construct of supplier socially responsible practices, including human rights, labour practices, codes of conduct and social audits. In ref. [ 6 ], the authors suggest that a positive effect on supply chain sustainability performance could be achieved when firms adopt environmental purchasing and sustainable packaging practices.

The concept of SSCM includes material, information and capital flows; cooperation across the supply chain; economic, environmental and social performance; and customer and stakeholder requirements [ 2 ]. The extant body of literature portrays a variety of different SSCM practices, but all have one central objective, namely, the improvement of supply chain sustainability performance. A total of 96 SSCM practices were identified in the literature from 21 papers. In order to conceptualize and develop a sound construct based on the literature and on [ 11 ], five practices that cover the aspects of SSCM emerged: (1) strategic orientation; (2) supply chain continuity; (3) collaboration; (4) risk management; and (5) pro-activity. This set of practices emphasizes enhancing the relationships among supply chain partners, the flow of goods and information, and the sustainability aspects.

Despite the major aspects of SSCM that the above practices cover, it should be highlighted that the set of practices that will be described below is not considered complete. Several other practices that have been discussed previously are investigated in the extant literature. In this paper, the SSCM practices as proposed by ref. [ 11 ] are used for two reasons: (1) these practices are applied to food supply chains; and (2) the aim of this paper is to further enhance the empirical content of these practices.

Strategic Orientation

Strategic orientation refers to the commitment of organizations to SCM, as well as to their dedication to the Triple Bottom Line (TBL) concept [ 11 ]. In ref. [ 25 ], the authors proposed that, in order to create a sustainable supply chain, a management orientation towards sustainability is required. The balance of environmental, social and economic issues, i.e., the Triple Bottom Line (TBL), plays a crucial role for companies that want to implement a sustainability strategy [ 68 , 69 , 70 ], and support their decision making [ 11 ]. In ref. [ 21 ], SSCM practices in the automotive sector were investigated and found that supply chain orientation and the TBL approach are the most important practices for supply chain sustainability. Furthermore, ref. [ 20 ] conducted a survey to investigate the impact of SSCM practices from manufacturing companies in various sectors on dynamic capabilities and enterprise performance. Their results showed a positive relationship between supply chain strategic orientation and sustainability performance. In the food industry, ref. [ 11 ] found that TBL orientation, which is driven by the consumer’s demand, the company’s motivation and the stakeholders’ pressure, is addressing the sustainability needs of the food industry.

Supply chain continuity is related to the design and structure of the supply chain network [ 11 ]. Ensuring supplier continuity is identified as one of the top sustainable supply chain management practices for exemplar firms [ 25 ]. Continuity has to do with the interaction of supply chain members on a permanent base [ 11 ]. The core elements of supply chain continuity are the long-term relationships with supply chain partners, the supply chain partner development and the partner selection. Long-term relationships include trust and commitment among the supply chain members [ 25 ], which endeavours information sharing [ 71 ] and enhances the collaborative design of products or processes [ 55 ]. Supplier development refers to the improvement in supplier environmental and social performance [ 25 ]. In traditional supply chain management, the development of suppliers is found to be one of the best practices [ 72 ], which is also connected to sustainability through mentoring approaches [ 73 ]. In the food industry, for example, the assistance and teaching of new farming methods or the funding of costs related to more sustainable farming practices are included in the development of partners [ 74 ]. Partner selection is based on their supply chain competency [ 75 ] and their desire to develop sustainable practices [ 76 ]. Focusing on activities that enhance transparency, traceability, supplier certification and decommodisation is important for ensuring supplier continuity [ 25 ]. As ref. [ 25 ] (p. 48) describe, organizations that are pursuing continuity in their supply chains, “are trying to ensure that all members of their chain not only stay in business, but that they do so in a manner that allows them to thrive, reinvest, innovate and grow”. Furthermore, focal firms are positively affected by supply chain continuity due to the fact that the supply chain base is stable and capable [ 25 ]. Ref. [ 20 ] also found a positive relationship between supply chain continuity and sustainability performance.

Collaboration

The importance of collaboration in supply chains has been recognized as a key factor but also as a great challenge for supply chain success [ 77 ]. Collaboration goes beyond the traditional modus operandi between organisations. First of all, collaboration as an SSCM practice is not restricted only to new product development but also to the development and enhancement of business processes [ 11 , 67 ]. The literature suggests that efficient and responsive supply chains rely on the creation of close and long-term relationships and partnerships with various members of the supply chain in order to increase the customer value [ 77 , 78 ]. Joint development is a key enabler for long-term partnerships. Reference [ 11 ] defines it as the collaborative development of new technologies, processes and products. As ref. [ 79 ] point out, specific resources from each supply chain partner are required in order to jointly address sustainability issues. The implementation of collaborative development is based on knowledge sharing in order to enable the development of sustainable products and processes [ 55 ]. Moreover, suppliers and customers can jointly plan the decrease of their operations’ impact on the environment or support the information exchange and the logistical and technical integration [ 67 ]. Collaboration is also characterized by enhanced communication—a very important practice regarding the management of supply chain partners. The quality of information sharing is critical in order to achieve transparency in the supply chain [ 80 , 81 ]. Transparency regarding the origin and ingredients of food, the production methods, etc., is also important for consumers [ 82 ]. Despite the need for collaboration to achieve sustainable supply chain management, significant barriers arise that are mainly due to the complexity of supply chains. For example, ref. [ 77 ] found that the structure of the food industry and the nature of products have a negative impact on the intensity of collaboration and restrict it to the more tactical-operational, tactical and logistical level.

Risk Management

Supply chain risk management includes the adoption of risk mitigation practices to avoid exposure to risks [ 2 ]. The adoption of standards and certifications is identified as the most common risk management practice in the literature [ 11 ]. This is due to the fact that standards and certifications such as ISO 9001 and ISO 14001 can be applied to a broad range of sectors and they can also be managed (if companies wish) by external consultants, who enhance the level of credibility [ 83 ]. Monitoring of specific suppliers in order to explore their needs and identify their progress on specific goals [ 84 ] is another practice identified within the risk management category. As authors in ref. [ 11 ] mentioned, individual monitoring of suppliers is particularly important in food supply chains, where traceability is a crucial factor to guarantee sustainable production. Despite this fact, individual monitoring is not frequently addressed in the extant literature [ 11 ]. Pressure group management is another key characteristic of risk management, which can affect the company’s reputation or performance [ 85 ]. In ref. [ 2 ], it is pointed out that stakeholders such as NGOs and government should not only be monitored but actively engaged and managed through the implementation of specific practices that address their pressures. It should be noted that the interests of a company and its stakeholders do not always align, and their pressure is seen from a negative perspective [ 11 ].

• Proactivity

Proactivity refers to the actions taken by a company in order to control and manage a specific situation regarding sustainability before it happens, rather than responding to it after it happens. The literature shows that Life Cycle Assessment (LCA) is the most common tool of the pro-activity practice [ 11 ]. LCA is used to measure the environmental impacts of the life cycle of a product or service. While LCA is a commonly discussed topic in the literature, ref. [ 25 ] found that exemplar firms are using life cycle analysis at the basic level, and only to address the environmental impacts of the chain and not the social ones. Ref. [ 11 ] highlights the necessity of supply chain orientation for LCA. If supply chain orientation is not implemented, the information between the supplier, buyer and focal company will not be shared. As a result, joint contributions should be made by all members of the supply chain [ 11 ]. Stakeholder management is found to be one of the most frequent practices in the literature [ 11 ]. When companies decide to adopt proactive practices, the management of stakeholder requirements is acting as an important factor for performance, products and processes improvement [ 2 , 11 ]. Innovation is another key factor of proactivity and it has been investigated in the field of sustainable supply chain management literature [ 85 ]. Innovation includes the capability of a company to generate and implement new ideas and develop or apply new technologies. It is a prerequisite for dynamic market environments such as sustainable supply chain management [ 11 ]. An example of supply chain innovation is the adoption of new innovative technologies, such as the Internet of Things or Industry 4.0 tools, which make both internal and external processes more efficient and result in improved sustainability performance [ 61 ]. Learning from partners and stakeholders is another important dimension of proactivity. The acquisition of new knowledge is the key characteristic of learning. Companies can learn from supply chain partners, local communities, NGOs, government, researchers, etc. The authors of ref. [ 86 ] showed that when firms wish to implement a sustainability strategy, they should be pro-active in the first steps of the product’s development and in its whole life cycle. Overall, ref. [ 25 ] highlight that proactivity and commitment can only be effective if companies achieve an alignment between business models and environmental and social sustainability aspects. Ref. [ 11 ] further explains that in sustainable food supply chains, such as organic or fair trade, which are dynamic in nature and still young industries, proactive measures are necessary, since many new processes and technologies are under development.

3.1.3. Performance

Sustainability performance refers to how well an organisation achieves its environmental, economic and social goals. Most studies in the literature focus on the economic and environmental performance aspects, whereas the social dimension and the integration of the three sustainability dimensions are still lagging behind [ 2 ]. However, the review of [ 4 ] revealed a rising interest in studies that investigate the social dimension and the combination of all three dimensions; however, more research is needed in the field. The present section proposes sustainability performance as a three-dimensional construct. A more detailed discussion of the environmental, economic and social performance is provided below. In total, 684 SSCM measures were identified from 55 papers, which were grouped in the following three categories.

Environmental Performance

A wide variety of research papers has focused on the environmental performance of supply chains. As ref. [ 2 ] argues, this can be explained due to the fact that environmental issues have been on the research agenda for many years. This could be further supported by the fact that, in many countries, organizations are obliged to meet specific thresholds on their environmental impacts; e.g., toxi-chemical releases [ 87 ]. The most frequently used measure is related to either the reduction or avoidance of hazardous/harmful/toxic materials. The second most cited measure is water consumption, followed by energy consumption, recycled materials, Life Cycle Analysis (LCA) and environmental penalties. Energy efficiency, air emissions and greenhouse gas emissions are also some of the most cited measures in the literature.

A variety of other measures that appear less in the literature have addressed themes such as waste [ 79 , 87 , 88 ], environmental management systems, eco-design [ 89 , 90 ], biodiversity [ 87 , 91 ], etc.

Economic Performance

Economic performance is typically the most important factor that all companies are aiming to improve. Since the focus of this research is on supply chain management, the economic dimension is an integral part. In the context of SSCM, the comprehensive literature review in [ 2 ] shows that economic issues were addressed in all the studied papers. At this point, it should be mentioned that possible trade-offs between the three sustainability dimensions can occur. Especially for the economic dimension, economic incentives could be hidden behind a variety of environmental and social measures [ 87 ]. For example, economic performance measures such as procurement costs might increase when deciding to use environmentally friendly materials [ 4 ]. The most frequent measure regarding the economic performance is quality. Measures that focus on quality may refer to the quality of products provided by suppliers [ 87 ] or to the quality of the production process [ 73 ]. Sales, market share and profit are the second most frequent measures, followed by delivery time and customer satisfaction.

Other measures that appeared less in the literature include responsiveness [ 89 , 90 , 92 ] number of employees [ 93 , 94 , 95 ], transportation costs [ 95 , 96 , 97 ], etc.

Social Performance

As mentioned before, previous studies have revealed that little research has focused on the social performance of supply chains [ 12 , 98 ]. The authors of ref. [ 99 ] argue that this could be due to the fact that social issues are frequently hard to measure. The literature shows that only a few measures are frequently used confirming the fact that little attention has been given to the social dimension of SSCM. The most frequently used measure is recordable accidents followed by training and education and labour practices.

Other social issues that appeared in the literature include human rights [ 100 , 101 , 102 ], local communities influence [ 89 , 90 , 103 ], fair trade [ 57 , 100 , 104 ], philanthropy [ 105 ], etc. A recent study [ 106 ] has shed light on modern slavery in supply chains, a new area in the agenda of SSCM that has gained a lot of attention lately.

Table 2 lists the proposed constructs described in Section 3.1.1 , Section 3.1.2 and Section 3.1.3 , along with their definitions and supporting literature.

Proposed SSCM constructs, along with their definitions and supporting literature.

Figure 2 presents the SSCM theoretical framework developed in this study. A detailed description of the identified constructs is provided in the previous sections. Using literature support, this study has linked the developed constructs and proposed the expected relationships among them. The framework proposes that critical factors are influencing the implementation of SSCM practices, which in turn influence SSCM performance. CF is conceptualized as a three-dimensional construct (firm level, supply chain level and external level); SSCM practice is conceptualized as a five-dimensional construct (strategic orientation, continuity, collaboration, risk management and pro-activity); and SSCM performance is conceptualized as a three-dimensional construct (environmental, economic and social).

Proposed theoretical framework linking critical factors, practices and performance (based on the literature).

3.2. Case Study Results

The empirical results of the food supply chain case study reflect all the SSCM constructs that have been presented in the theoretical framework. In addition, some new “pioneering” SSCM practices emerged from the data. In Section 3.2.1 , the first research question is answered regarding the critical factors for engagement and implementation of SSCM practices. The second and third research questions are answered in Section 3.2.2 and Section 3.2.3 , by addressing which SSCM practices are implemented and what measures can be used for SSCM performance measurement in the food industry.

3.2.1. Critical Factors

The commitment and support of top management is reported as predominant firm-level critical factor for SSCM implementation. As highlighted, “sustainability is seen as an integral part for the future of our business. You cannot produce like there is no tomorrow, you produce because you want tomorrow to exist” (CSR Manager, SB). SSCM requires “proactive top management that understands that sustainability is an organizational commitment” ([ 25 ], p. 40). Indeed, top management is a critical firm-level factor for the promotion of SSCM and its absence may act as an obstacle for SSCM adoption [ 1 , 35 ].

Customer-driven orientations, in order to meet customer demands and needs, have been confirmed as critical factors of SSCM implementation, by all the interviewees. Previous studies have found that customer demands and requirements drive the development and implementation of SSCM practices [ 35 , 39 , 47 ]. For example, ref. [ 19 ] found that customer expectations are some of the most important driving forces for SSCM implementation. Similarly, ref. [ 47 ] have confirmed that customer demand and expectations are market drivers for corporate supply chain responsibility. In other words, adapting to what customers want is necessary for the implementation of SSCM at all supply chain stages.

According to the managers and the companies’ records, expertise and knowledge on environmental and social issues of supply chains is required to implement SSCM practices. Knowledge about how suppliers and other partners work regarding sustainability, is a critical SSCM factor that exemplar firms are adopting to improve their entire supply chains [ 25 ].

Employee training and development was also confirmed by both companies as another important firm-level critical factor. All key informants highlighted the continued efforts of their companies to offer a variety of programs in order to improve employee satisfaction and raise the sustainability awareness. Training and development can lead to engagement in SSCM practices, which, as mentioned by the interviewees, is a crucial part of the corporate strategy. It was evident by both the participants and the companies’ records that training and development programmes improve job performance and reduces errors and waste, which was also confirmed by the study of [ 18 ].

Efficiency in operations and material management was mentioned by the participants. Efficient energy management and electricity generated from renewable energy sources were the top mentioned factors of SSCM. Another element of efficiency is technology. Both companies exploit the available technologies to improve and optimize operational processes. This leads to cost savings and resource reduction and thus offers the ability for new investment plans.

The sampled supply chain is involved in traceability actions with their suppliers. Previous literature suggests that traceability is a new form of information sharing [ 25 ]. There is a requirement for information sharing on the living conditions of the animals, on the production of products, on the materials used, the locality information, information related to product labelling, etc. As reported, clear information about the products and their ingredients are provided on the front and back of the packages.

According to the interview data, the key to successful solutions to the daily problems is trust. Trusted partnerships and long-term cooperation build relationships of trust and confidence with suppliers. In this way, both companies achieve their goals, while at the same time “pushing” their suppliers to develop and improve as individuals. The same logic applies to the customers as well. Several systems are applied in the sampled companies, such as, compliance management system as well as anti-corruption and antifraud systems. In general, both companies are trying to create a climate of mutual trust among their stakeholders (employees, customers, suppliers, local communities, etc.).

Both companies have managed to establish a common supply chain strategy with their supply chain partners. The improvement in environmental and social standards across the supply chain is in the core element of the SSCM strategy. The organisations implement a sustainability strategy in their partnerships that includes goal-oriented actions. As reported, the suppliers are a crucial part of the supply chain, and through continuous dialogue with them, the added value of the products and services reducing one’s environmental footprint and effects on society is enhanced. Geographical distance was not mentioned by the participants. However, both companies use local sourcing in more than 80% of their operations. This creates additional added value in the local economy, with the indirect creation of jobs.

The interview data revealed that legislation requirements very often force companies to transform their business by applying sustainable practices; e.g., water saving. This is further identified in the secondary data, where strong focus is given on information regarding the legal penalties or fines for non-compliance with environmental and social regulations. Information is also provided regarding the compliance to European and national legislation on consumer products and the non-promotion and communication to minors (aged under 18 years old). The literature suggests that regulations and legislations can act as strong driving forces for the implementation of SSCM practices [ 39 , 43 ]. Examples, such as the British Petroleum (BP) oil spill, have shown that there is as huge negative impact on the supply chain economic performance, estimated at around $90 billion, including civil and criminal penalties [ 107 ].

Furthermore, the trends of stakeholders undoubtedly constitute an important pressure as they can also change a company’s strategy. For example, an interviewee mentioned that when there was an intensive debate about obesity, the company realized that it could not ignore it and decided to develop new products for consumers who do not want to get extra calories. In this way, the consumer had the choice of choosing the suitable product regarding his/her wishes. The sampled companies engage stakeholders in active dialogues throughout the year, to determine and redesign their sustainability strategy and actions and understand how to meet their needs and expectations. Stakeholder management is critical for maintaining a healthy and sustainable business. As declared with the CSR reports of the two companies and corroborated by the interview data, producing a positive value for stakeholders and creating the conditions for a healthy competitive environment enhance sustainable development.

The risk of changing product quality after production is mentioned as a key external critical factor for implementing SSCM practices. As reported, a company makes significant investments in order to offer the customer the right product, in the right package, at the right point of sales and at the right price, with its primary concern being safety. Another interviewee highlighted that the company is developing and implementing systems, standards and practices to ensure food quality and safety and avoid actual and reputational risks such as child labour.

3.2.2. Practices

The data analysis suggests the development of two main groups of practices: the traditional SSCM practices and the pioneering SSCM practices. The first group includes the SSCM practices as identified in the literature, while the second group encompasses SSCM practices that are adopted by leaders. The term “pioneering” is used only to describe these practices in the Greek food industry context. In the following sections, a description of both groups of practices is provided.

Traditional SSCM practices

Collaborations with supply chain members such as suppliers and customers as well as with a range of stakeholders such as NGOs and other entities are identified as key practices that help both companies and their supply chains to achieve sustainability goals. Long-term collaborations and contact with suppliers and stakeholders create relationships of trust and confidence. Development and improvement of suppliers as individuals is another characteristic of collaboration that emerged from the data. Joint development and training of suppliers is found to add value in the supply chain management performance. The data revealed that the companies are already deploying traceability practices for specific products. In parallel, they both are in the process of digital transformation, which will help them to increase supply chain traceability, transparency, quality, speed and efficiency.

Practices regarding suppliers’ and external partners’ selection are reported in the continuity category. According to an interviewee from SB, “There are guiding principles for all suppliers which include a wide range of requirements such as the confirmation that children are not working at a supplier’s company”. SB is implementing a “continuous development” approach, which deploys corrective actions to ensure that all suppliers comply with the company’s environmental, social and labour policy. Furthermore, the data suggest that partnering with reliable suppliers, especially in quality and safety issues, is necessary for a continuous relationship.

Strategic orientation

As reported, both companies are engaged in strategic supply chain management, which promotes the balance among environmental, economic and social issues. The data reveal that an SSCM strategy was already in place and three common characteristics were identified. First, a continuous business model alignment with economic, environmental and social issues is in place. For example, SB has re-designed a series of their products towards reducing plastic in packaging and this resulted in environmental and economic benefits, while at the same time allowed the company to apply similar techniques to other products. This is consistent with previous studies that found that alignment of environmental, social and economic goals is needed for managerial orientation towards sustainability [ 25 ]. The second and third component is that both companies treat suppliers as key strategic partners and focus on strategic sustainability issues related to the local communities.

Risk management

The implementation of management systems is used as a risk management tool for both companies. Food quality management systems (e.g., ISO 22000), environmental management systems (e.g., 14001) and health and safety (OHASAS 18001) are identified as key risk analysis tools. Furthermore, a strict supplier selection criteria system is supporting the risk management practices along with supplier monitoring through tactical inspections. Apart from the risk mitigation outcomes, tactical inspections are a pre-requisite for the successful interaction and long-term relationships among the supply chain members.

In this group of practices, the key component is to go beyond compliance with current legislation requirements by engaging in more advanced sustainable practices. Product innovation (e.g., products with reduced calories) and process innovation especially in the logistics domain are identified as key for SSCM. Supplier codes of conduct, including environmental, health and safety, labour and social issues, as well as partners’ coaching to adopt and implement SSCM practices are also included in proactive practices. Finally, energy- and water-saving practices and efficient fleet management are implemented to reduce the negative outcomes. Another set of practices that is related to proactivity, as stated by the CSR Manager of the SB and the Logistics Project Manager of the SM, is employee welfare, human rights practices, and the supporting actions for young people and local communities.

Pioneering SSCM Practices

• Conversation

Sustainability is part of the daily conversation in the two companies. Discussions of noneconomic issues is shared across all departments. As the CSR Manager of the SB company mentioned, “the basic principle in our company is social and environmental responsibility in our daily transactions”. Daily conversations about sustainability issues are part of all decision-making processes in a way that all employees consider social and/or environmental impacts of their decisions. As ref. [ 25 ] (p. 51) proposed, “management orientation is evidenced by sustainability being part of the day-to-day conversation”.

• Local sourcing

Local sourcing was evidenced by a focus on sourcing from Greek suppliers in more than 80%. Clear sustainability benefits of local sourcing include minimization of transport, increase of freshness and contributions to environmental and social improvements.

• Investing in Human Resources

Investing in human resources is considered a key SSCM practice. As in previous studies on sustainability leaders [ 25 ], the internal focus in this sample is on employee investments. Both companies provided information regarding their programmes for employee training, skills development and benefits. They both recognised positive outcomes regarding the employees’ personal development and well-being and their commitment to the organisations’ goals. As an interviewee mentioned, “investing in employee training and development not only serves as a motivation, but it also enables the organization to create a highly skilled workforce”.

3.2.3. Performance

By analysing the companies’ records, it became evident that sustainability performance was measured through specific indicators and standards. More specifically, both companies follow the GRI and UN Global Compact principles. This is evidenced by the sustainability reports, which reveal that the companies are adapting to international sustainability reporting standards. This should be no surprise, since both companies are sustainability leaders.

Both companies have mentioned that SSCM is related to a direct increase in costs. Many of the aforementioned practices, apart from the financial resources, include investments in human, and time resources. For example, practices regarding suppliers’ and external partners’ selection, such as the suppliers guiding principles of the SB, which require the confirmation that children are not working at the supplier’s company, as well as the tactical supplier inspections, increase costs. However, as the CSR Manager of SB mentioned, “sometimes you pay more to have the best suppliers and this contributes to added value for costumers, which increases customer loyalty”. Supporting local suppliers to adopt SSCM practices (employee protection and security, human rights, etc.) also contributes to the local economy through indirect job creation.

On the contrary, energy-saving practices are found to have a positive financial impact by means of cost reduction, which increases the profit rates. This is due to the fact that energy-efficiency investments are producing results from the first day of implementation. For instance, both companies have invested huge amounts in LED lighting, which is considered a highly energy-efficient technology.

Quality improvement is another important economic factor that both companies are engaged in. For instance, SM has mentioned that compliance with quality standards and reduction of defective products are key quality measures.

Not surprisingly, sales and market share, is also found to be a key economic measure. Other measures discussed under the economic dimension are the annual R&D investments, productivity, delivery time and flexibility.

As expected from both companies, as sustainability leaders, they have environmental performance systems in place that manage not only the environmental “basic” indicators (hazardous/harmful/toxic materials, energy, water, CO 2 emissions, compliance to standards, environmental accidents and use of recycled materials) but the advanced ones as well, such as the re-design of products towards a reduction in plastic and the reuse of it through circular processes. A key characteristic of both companies is that most of the indicators are measured at the organizational level. For example, energy use is measured in both companies’ facilities but not in their suppliers’ operations. It is also reported that the energy consumed comes from renewable energy sources at a level of 100% in SB’s facilities and 97% in SM’s facilities. Managers from SM have reported that the company is planning to measure the indirect emissions of its supply chain. As [ 9 ] propose, a useful tool to measure the impact of a supply chain as a whole is life-cycle analysis (LCA).

A variety of other measures have addressed themes such as waste recovery [ 20 ], waste [ 79 , 87 , 88 ], environmental management systems, eco-design [ 7 , 89 , 90 ] biodiversity [ 87 , 91 ], etc.

In the social sustainability dimension, the data suggested indicators such as product safety, employee accident rates, employee training rates, health and safety issues, employment contribution, employee benefits, loyalty and turnover rate, corporate image, human rights screening (suppliers and contractors) and community support. Several projects both internal and external are implemented in both companies. For example, an excellent working environment that is fair, safe and enjoyable with prospects for development (such as job rotation, promotions, new roles, etc.) is a key performance measure for SB. From an external point of view, supplier social assessment is performed from SB regarding the suppliers’ human right policies and broader social issues. Furthermore, SM reported that local community support in the form of volunteering or charity actions is another key performance indicator.

Table 3 presents the SSCM aspects as identified in the case study.

Aspects of SSCM as identified in the case study.

3.3. Discussion

The results of this study offer empirical evidence regarding the identified constructs and their interrelationships. More specifically, the data analysis suggests a model of SSCM in the food industry, providing a first step toward defining three constructs (critical factors, practices and performance) that can create sustainability in the food industry. The proposed model is depicted in Figure 3 .

Conceptual model of sustainable supply chain management in the food industry.

The model is developed based on the extant literature and the case study data. Figure 3 presents specific relationships between the constructs, which contribute to a better understanding of SSCM in the food industry. In the following paragraphs, the relationships of the proposed constructs are conceptualized in propositions that need to be tested in future research.

The ability of a company to identify and understand the factors that enable and inhibit the creation of sustainability across supply chain is critical for SSCM. A variety of SSCM critical factors is identified and categorized at the firm level, the supply chain level and the external level. These factors are linked to the implementation of SSCM practices. In line with prior literature, the commitment of top management or the knowledge and expertise regarding sustainability are identified as important firm-level critical factors for SSCM. For example, ref. [ 1 , 35 ] suggest that the lack of top management commitment and support hinder the development of SSCM. SSCM requires “proactive top management that understands that sustainability is an organizational commitment” [ 25 ] (p. 40).

At the supply chain level there is evidence that information sharing and trust between partners are two of the key critical factors for implementing SSCM. The literature posits that that information sharing enables the development of new ideas regarding sustainability and enhances collaboration throughout the supply chain [ 18 ]. On the opposite side, the lack of information sharing is found to have a negative impact on SSCM implementation [ 41 , 42 ].

Regarding the external environment, three key factors have been confirmed by the dataset: compliance with international and national regulations, stakeholder management and reduction in actual and reputational risk. The identification, engagement and communication with customers, local community and NGOs were reported as critical factors for the successful implementation of SSCM practices. This is consistent with prior literature which confirmed that stakeholders are driving forces for the integration of SSCM practices [ 19 ]. Especially in the food retail industry NGO pressure is critical for the adoption of SSCM [ 53 ].

Based on the above, the first set of propositions is developed below.

SSCM critical factors are directly related to the implementation of SSCM practices.

Firm-level critical factors are directly related to the implementation of SSCM practices.

Supply chain-level critical factors are directly related to the implementation of SSCM practices.

External critical factors are directly related to the implementation of SSCM practices.

Considering the adopted SSCM practices, the findings suggest two main groups, namely, the traditional SSCM practices and the pioneering SSCM practices. Traditional SSCM practices include the five categories proposed in the literature. This is not a surprise, since the sample of this study is comprised by leaders in sustainability. In this case study, the SSCM practices as proposed by [ 11 ] are used as a key starting point and as a guiding tool for developing a model of SSCM in the food industry. What is interesting in this case study, is the possible trade-offs between the SSCM practices. For example, the focus on supplier continuity requires long-term relationships which is a key element of collaboration. This is also consistent with prior literature which suggests that supply base continuity long-term relationships are critical for the successful implementation of SSCM [ 108 ]. Continuity was also evidenced by a focus on supplier risk management. Both companies have in place a supplier selection criteria system, which is also related to the supplier codes of conduct that comprise environmental, health and safety, labour and social issues. Regarding the three identified pioneering SSCM practices (conversation, local sourcing and HR investments), it should be noted that they could have been encompassed in the traditional SSCM practices. However, it was decided to be separately presented since both companies engage in these practices in significant amounts. Furthermore, the purpose was to show what sustainability leaders in the food industry are doing regarding SSCM. In no way do these three practices constitute something new or unique.

The findings underline that SSCM performance is linked to SSCM practices. Despite the fact that all participants agreed on a direct increased cost of implementing SSCM, their general perspective was that SSCM practices have the ability to enhance environmental and social performance. This is also supported by [ 6 ], who found that environmentally friendly purchasing and sustainable packaging have a positive effect on sustainable performance. Another example based on the results is food safety, which is linked to improved sustainability and can be achieved through traceability practices. Evidence of similar results is also provided by [ 34 ], who found that traceability practices in the meat supply chain are closely associated with social sustainability and food safety. It can also be argued that traceability is the end-result of sharing information, which is related to enhanced supply chain performance [ 25 ]. Based on the above observations, the following propositions are developed.

SSCM practices are positively associated with sustainability performance.

Strategic orientation is positively associated with sustainability performance .

Continuity is positively associated with sustainability performance.

Collaboration is positively associated with sustainability performance.

Risk management is positively associated with sustainability performance .

Pro-activity is positively associated with sustainability performance.

Conversation, is positively associated with sustainability performance.

Local sourcing, is positively associated with sustainability performance.

Investing in HR is positively associated with sustainability performance.

Another interesting finding is the interrelationships between the three dimensions of sustainability performance. The data suggest that environmental performance improvements, such as energy efficiency practices, have visible cost reductions in the short term. This contradicts the results of [ 24 ], who found that in the food industry environmental performance is not affecting costs directly. Continuing with a study in the Italian meat supply chain, ref. [ 34 ] found that SSCM practices, such as cleaner technologies, offer a competitive advantage, since they contribute to improved economic and environmental or social performance. Ref. [ 109 ] also found a positive correlation between corporate social performance and corporate financial performance. Based on the above arguments, the following propositions are developed.

Environmental performance is positively associated to economic performance.

Social performance is positively associated to economic performance.

4. Conclusions

4.1. theoretical contributions.

This research has examined the SSCM critical factors, practices and performance through a literature review and a case study comprised of sustainability leaders in the food industry. The study has identified the SSCM critical factors and practices that sustainability leaders implement and what measures are used in sustainability performance in the food industry. In line with ref. [ 32 ], who highlights the deductive nature of case studies, this research investigated the applicability and validity of the three SSCM constructs as identified in the literature review, in a specific Greek food supply chain. The case study implies direct and indirect links among the three key constructs, namely, SSCM critical factors, SSCM practices and sustainability performance. Furthermore, in line with the developed propositions, the three constructs are conceptualised within a model that needs to be quantitatively tested.

It can be argued that it is not a surprise that the two sustainability leaders are more committed to SSCM. Both have identified common factors that are critical for developing SSCM practices. This study has also identified a new set of pioneering SSCM practices in the Greek food industry. Daily conversations, local sourcing and investing in HR are common practices for SSCM leaders in the Greek food supply chain, however industry specific.

The developed SSCM conceptual model can be exploited by researchers that wish to investigate the proposed constructs individually or together, both at the firm level and the supply chain level, and either through quantitative (surveys) or qualitative research methods (replicate the case study in other geographical locations or other industries). Researchers may also take advantage of the developed model and use it as an evaluation framework or as an SSCM roadmap for the design of future research projects.

4.2. Managerial Implications

Apart from the theoretical contributions, this study provides some managerial implications regarding the deployment of the proposed model. While the identified constructs in this research are not new and can be characterized as SSCM traditional, they have been studied in a food supply chain considering all the three sustainability dimensions. The developed model can be used by companies in the food industry that want to promote or determine the best way to develop SSCM and improve their sustainability performance. The results can be utilized by food industry professionals and assist them in the development of SSCM by identifying the critical factors of SSCM implementation, the practices adopted, and the sustainability performance measures.

4.3. Limitations and Future Research Directions

This study, as in any other research, suffers from limitations that will be presented along with future research propositions. First, the sample is small, industry and location specific, and the results cannot be transferred or used to generalize the overall food industry. Future studies may conduct research in other industries or world regions, using larger samples, in order to achieve generalization of the results. Second, this study focused on food sustainability leaders. It is likely that in more typical organisations—not sustainability leaders—different SSCM factors, practices and performance measures will be identified. Third, the traditional and pioneering practices should be investigated in other industries to check their applicability as well as the possible trade-offs. Finally, in this study, specific interrelationships among the constructs are addressed. However, the small sample does not allow for deeper investigations. Future research should examine the importance of each of the constructs and the strength of their inter-relationships.

Interview Protocol

• (1) General information about the company
• What are the factors that push the company to implement SSCM practices?
• What are the factors that hinder the company to implement SSCM practices?

Supply chain continuity

Pro-activity

• What measures/indicators does your company use to measure SSCM performance?
• How has the implementation of SSCM practices affected the environmental, social and economic performance of your company?
• Is there any observed relationship between environmental, social and economic performance (win–win, win–lose)?

Funding Statement

This research received no external funding.

Author Contributions

Conceptualization, T.M. and K.G.; methodology, T.M. and K.G.; validation, T.M. and K.G.; formal analysis, T.M.; investigation, T.M.; resources, T.M.; data curation, T.M. and K.G.; writing—original draft preparation, T.M.; writing—review and editing, T.M. and K.G.; visualization, T.M.; supervision, K.G.; project administration, T.M. All authors have read and agreed to the published version of the manuscript.

Institutional Review Board Statement

The study was conducted based on the principles of the Committee for Research Ethics of the University of Macedonia, that was established according to Chapter E’ (Articles 21–27) of Law 4521 (Government Gazette vol. A ’38/2-3-2018).

Informed Consent Statement

Informed consent was obtained from all subjects involved in the study.

Data Availability Statement

Conflicts of interest.

The authors declare no conflict of interest.

Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.

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The application of artificial intelligence and big data in the food industry.

1. Introduction

1.1. the early situation in the food industry, 1.2. the current state of the food industry, 1.3. the importance of food safety, 1.4. digital transformation in the food industry, 2. big data in the food industry, 2.1. applications of big data in the food industry, 2.1.1. application of personalized marketing and recommendation system, 2.1.2. consumer behavior analysis and forecasting, 2.1.3. the utilization of big data analytics in supply chain management, 2.1.4. application of forecasting models and machine learning algorithms in demand forecasting, 2.2. the bottleneck of big data applications for the food industry, 2.3. blockchain technology, 3. artificial intelligence in the food sector, 3.1. knowledge-based expert systems in the food industry, the future and challenges of expert systems, 3.2. fuzzy logic systems, 3.3. adaptive neuro-fuzzy inference system (anfis) technology, 3.4. near-infrared spectroscopy technology combined with artificial intelligence, 3.5. application of computer vision systems in the food industry, 3.6. artificial intelligence combined with smart sensors for real-time inspection in the food industry, 4. future trends and challenges for artificial intelligence applications in the food field, 4.1. future development direction and outlook, 4.1.1. application prospects of emerging technologies, 4.1.2. possible directions for innovation and improvement, 4.1.3. exploration of feasibility and sustainability issues, 4.1.4. future challenges ahead, 5. conclusions, author contributions, institutional review board statement, informed consent statement, data availability statement, acknowledgments, conflicts of interest.

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Ding, H.; Tian, J.; Yu, W.; Wilson, D.I.; Young, B.R.; Cui, X.; Xin, X.; Wang, Z.; Li, W. The Application of Artificial Intelligence and Big Data in the Food Industry. Foods 2023 , 12 , 4511. https://doi.org/10.3390/foods12244511

Ding H, Tian J, Yu W, Wilson DI, Young BR, Cui X, Xin X, Wang Z, Li W. The Application of Artificial Intelligence and Big Data in the Food Industry. Foods . 2023; 12(24):4511. https://doi.org/10.3390/foods12244511

Ding, Haohan, Jiawei Tian, Wei Yu, David I. Wilson, Brent R. Young, Xiaohui Cui, Xing Xin, Zhenyu Wang, and Wei Li. 2023. "The Application of Artificial Intelligence and Big Data in the Food Industry" Foods 12, no. 24: 4511. https://doi.org/10.3390/foods12244511

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Please note you do not have access to teaching notes, restaurant and foodservice research: a critical reflection behind and an optimistic look ahead.

International Journal of Contemporary Hospitality Management

ISSN : 0959-6119

Article publication date: 10 April 2017

The purpose of this paper is to present a review of the foodservice and restaurant literature that has been published over the past 10 years in the top hospitality and tourism journals. This information will be used to identify the key trends and topics studied over the past decade, and help to identify the gaps that appear in the research to identify opportunities for advancing future research in the area of foodservice and restaurant management.

Design/methodology/approach

This paper takes the form of a critical review of the extant literature that has been done in the foodservice and restaurant industries. Literature from the past 10 years will be qualitatively assessed to determine trends and gaps in the research to help guide the direction for future research.

The findings show that the past 10 years have seen an increase in the number of and the quality of foodservice and restaurant management research articles. The topics have been diverse and the findings have explored the changing and evolving segments of the foodservice industry, restaurant operations, service quality in foodservice, restaurant finance, foodservice marketing, food safety and healthfulness and the increased role of technology in the industry.

Research limitations/implications

Given the number of research papers done over the past 10 years in the area of foodservice, it is possible that some research has been missed and that some specific topics within the breadth and depth of the foodservice industry could have lacked sufficient coverage in this one paper. The implications from this paper are that it can be used to inform academics and practitioners where there is room for more research, it could provide ideas for more in-depth discussion of a specific topic and it is a detailed start into assessing the research done of late.

Originality/value

This paper helps foodservice researchers in determining where past research has gone and gives future direction for meaningful research to be done in the foodservice area moving forward to inform academicians and practitioners in the industry.

• Hospitality management
• Restaurants
• Food and beverage
• Foodservice research

DiPietro, R. (2017), "Restaurant and foodservice research: A critical reflection behind and an optimistic look ahead", International Journal of Contemporary Hospitality Management , Vol. 29 No. 4, pp. 1203-1234. https://doi.org/10.1108/IJCHM-01-2016-0046

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150+ Food Research Paper Topics Ideas for Students

When writing a research paper on food, there are many angles to explore to choose great research topics about food. You can write argumentative essay topics on food processing methods or search for social media research topics . Moreover, the food industry is advancing, and food styles are changing – another inspiration for an outstanding research topic about food. In other words, if you are looking for your ideal topic for food research , there are many places to look.

How to Choose the “Ideal” Food Research Topics

150+ ideas of experimental research titles about food, research title about food processing.

• Interesting Research Topics on Fast Food

Research Title about Food Industry

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Nevertheless, it can be hard to decipher what characterizes a good example of a thesis title for food. Hence, this article will briefly explain what factors to look for in a research title about food so-to-speak. Then, we will provide up to 150 food topics you can explore.

Personal interest is a vital factor to consider when sourcing the best thesis title about food . If you’re choosing a research title about cookery, you want to ensure it is something you’re interested in. If you’re unsure where your interest lies, you can check out social issues research topics .

Also, the availability of information on the topic of food is important in any research, whether it’s a thesis statement about social media or nutrition topics . Furthermore, choose several food topics to have options if one thesis about food doesn’t work out. Last but not least, ensure your chosen topic about food is neither too broad nor too narrow.

If you are unsure what title about food to work on for your research paper, here we are. Below are some of the best examples of thesis titles or professional thesis writers about food for students and researchers.

• Plant sterols in treating high cholesterol
• Is skipping breakfast healthy?
• Macrobiotic diet: advantages
• Food trendmakers
• Chocolates and emotions: the connection
• Are trans fats carcinogenic?
• Does green tea burn calories?
• Humble lentil: a superfood?

Interesting Research Topics Fast Food

• Fast foods: impact on living organisms
• Food court restaurants
• Misconceptions about fast foods
• Is McDonald’s healthy?
• Fast food: a social problem?
• National cuisine
• Fast food: effect on the liver
• Fast food education
• Students’ nutrition
• Fast food in children’s diet
• Food and 3D virtual reality
• The contemporary hotel industry
• Food and fashion
• Food in different cultures
• Can food be used for cultural identification?
• Trends in food box consumption
• Information innovation in the food industry
• The food industry in developing countries
• Proper nutrition
• History and origin of food traditions
• Can dietary supplements increase bone density?
• Why nutrition science matters
• Organic food: impact on nutrition
• Antimicrobial resistance
• Services ensuring food safety in the US
• Food safety violations in the workplace
• pH balance impacts flavor
• Animal testing should be abolished
• Does overeating suppress the immune system?
• Lifestyle-related chronic diseases
• Food justice
• Government’s involvement in food justice
• Dietary deficiencies
• Spice rack organization
• Nutrients for body development
• Milk for kids: more or less?
• Organic food and health
• Animal-sourced foods: beneficial or dangerous?
• Continental dishes
• Continental dishes vs. Indian spices
• Food factor in national security
• Junk food vs. healthy food
• Environmental food safety
• Safety and control of food colors in the food industry today
• Criteria and scope of food security
• Ensuring food security
• Cooking technology
• Food quality of agricultural raw materials
• Problems and solutions to food safety
• Food security: the theory and methodology
• Recent labeling food innovations
• Health benefits of genetically modified foods
• The vegetarian diet
• Caloric foods
• Fast food affects on health
• Food allergies
• Fast foods: nutritional value
• Food in the 21st century
• The Slow Food movement
• Doughnut’s history
• Food safety: role in gene pool preservation
• Controlling synthetic colors used in food
• Food assessment and control
• Food: its influence on pharmacotherapy’s effectiveness
• Human rights to balanced nutrition
• Quality of food products in urban areas
• Food in rural areas vs. urban areas
• Food security in Uganda
• Food safety: developed vs. developing countries
• Food factor in biopolitics
• Corn starch in baking: the importance
• Bacteria concerns in baking: Clostridium botulinum
• Normal butter vs. brown butter
• Matcha in Japanese pastry
• Sweet in baked desserts
• Effect of flour type on cake quality
• Sugar vs. stevia
• Why so much sugar in packed cakes?
• Carob is use in baking
• Coca-Cola baking: is it safe?
• Cooking schools
• Protein food preservation
• Food preservation techniques
• Vegan vs. non-vegan
• Caffeine in drinks
• Plastic and food quality
• History of carrot cake
• Turmeric: health properties
• Japanese tea ceremonies
• Healthy sugar substitutes
• The popularity of plant-based diet
• Food steaming: history
• CBD-infused foods
• Achieving the umami flavor in cooking
• Climate and diet
• Quick-service restaurants: impact on life expectancy
• Drinking and Judaism
• Chinese tea: a historical analysis
• Meat canning
• Resistance of meat to antimicrobials
• Eliminating botulism
• Reducing food allergies
• Avian influenza
• Vitamin D nutrition: the worldwide status
• Nutritional supplements are available for the poor
• Food science: importance in human nutrition
• Amino acids and muscle growth
• Poor nutrition and bone density
• Women and diet
• Tea vs. coffee
• Is tea addictive?
• Cholesterol: myths
• Sugar vs. sweeteners
• Keto diet: effect on health
• Food sensitivities in children
• African superfoods
• Spirulina: the properties
• Wine in French cuisine
• Garlic and onions
• Stored foods
• Preventing food poisoning
• Food addiction
• How to fight against food waste
• Aqueous environment: the toxicity
• Fast food in hospitals
• The risks associated with junk
• Food culture and obesity
• The link between fast food and obesity
• Burgers: are they sandwiches?
• Food additives
• History of curry
• Freezing dough: impact on quality
• Best pizza Margherita recipe
• Making low-calorie food tasty
• Jamaica and British cuisine
• Picked food in India
• How to eat eggs
• Egg poaching
• Italian pasta: types

From food innovation research titles to food sustainability research topics , there are many areas of the food industry to explore. With the list of topics and tips for choosing a topic provided here, finding your ideal topic should be easier.

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Food Market Research Reports & Industry Analysis

The global food market is a massive industry that plays a crucial role in our everyday lives. As the world population continues to grow, so does the demand for food. The food industry is constantly evolving, with new technologies and trends emerging every year. To stay ahead of the curve, it's essential to have access to reliable food industry analysis. That's where MarketResearch.com can help.

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• Vast research collection: Our reports cover a wide range of markets, including baked goods, breakfast foods, canned foods, dairy products, fish and seafood, food packaging, food processing, frozen food, fruits and vegetables, functional foods, ingredients, meat and poultry, snack foods, and much more.
• Essential market data: Whether you're an entrepreneur, a marketer, or consultant, our reports give you the information you need to make informed decisions by providing data on market sizing, growth forecasts, segmentation, drivers and challenges, the competitive landscape, and competitor strategies.
• Insights into disruptive trends: In addition, our reports cover the latest developments in the industry, from plant-based alternatives to emerging food technologies. With our research, you can gain a competitive advantage in an ever-shifting market.

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Food Industry Research & Market Reports

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Food Industry: An Introduction

2019, International Journal of Trend in Scientific Research and Development

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Trends in Food Science & Technology

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Indian Food processing sector is a key industry in India which is also the second largest producer of food products in the world next to China. The growth in food processing industry will lead to overall development in the economy as it will establish a vital synergy between the two main pillars of agriculture, economy and industry. As India is making a shift in its policy to "Make in India "and numerous schemes and development have opened up for food processing industry. The paper aims to study the status of food processing sector and the key driver which will boost its growth and role in Indian economy.

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Food processing has been studied in mainstream economics from the standpoint of an industrial activity, which has strong backward linkages with agriculture. We start the generalized notion of arbitrage as the central economic theory for establishing a successful industry in food processing. This, we find, is not sufficient to explain different regional outcomes in this industry. The industry has many sub-sectors, such as grain or meat-based or those linked with fruit and vegetable processing. These subcomponents in the processed foods industry generate value as we move from basic to advanced processing. This chapter discusses in brief the history of this industry and goes on to introduce the notion of a product network in processed food manufacturing. It provides brief snapshots of a collection of these. Details regarding technologies, costs and other supply-side signatures in these networks are studied. This exercise is needed to understand the real challenge in starting a successf...

Transstellar Journals

Food processing is the conversion of agricultural products to stuff which have specific textural, nutritional, and sensory properties using commercially viable methods. Various keys factors like easy marketing & distribution, consumer's convenience, hygiene, enhanced food consistency, throughout availability of the product are causing the industry to gear up. It was seen that the post-independence era in India observed high growth in the processing sector especially after 1980. The objective of this study is to study the importance of food processing industry in Indian economy. The current study is based on secondary data. The relevant material and secondary data were collected through various sources. Thus, it will help in implications of various policies for better and smooth functioning of the sector.

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