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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.


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.

figure 1

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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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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Big data in the food supply chain: a literature review

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The emergence of big data (BD) offers new opportunities for food businesses to address emerging risks and operational challenges. BD denotes the integration and analysis of multiple data sets, which are inherently complex, voluminous and are often of inadequate quality and structure. While BD is a well-established method in supply chain management, academic research on its application in the food ecosystem is still lagging. To fill this knowledge gap and capture the latest developments in this field, a systematic literature review was performed. Forty-one papers were selected and thoroughly examined and analysed to identify the enablers of BD in the food supply chain. The review primarily attempted to obtain an answer to the following research question: “What are the possibilities of leveraging big data in the food supply chain?“ Six significant benefits of applying BD in the food industry were identified, namely, the extraction of valuable knowledge and insights, decision-making support, improvement of food chain efficiencies, reliable forecasting, waste minimization, and food safety. Finally, some challenges and future research directions were outlined.

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1 Introduction

The food industry is an integral part of every economy and plays a critical role in supplying the necessities for human survival and provides consumer choice (Turi et al. 2014 ). According to estimates, US$14 trillion of foods is produced, packaged and sold worldwide every year and encompasses a multitude of transactions between suppliers, retailers and consumers (Ji et al. 2017 ). At the same time, the global food system is still encountering a series of serious challenges such as the increase of world population, rapid urbanization, ageing of countries’ populations, sustainability, and the alarming global change of the environment (Cerqueira et al. 2019 ). Similarly, the fragmented nature of global food supply chains presents an additional challenge to respond to consumers’ requirements in terms of food safety, quality, and authenticity. The food supply chain is a dynamic system encompassing food brands, primary producers, processors, regulators, third-party actors and other resources engaged in various processes and governance (Yu and Nagurney 2013 ). With the fast pace of technology developments, the conventional ways of managing and delivering food products to markets and consumers are evolving. Today, technology is viewed as a critical enabler, and Nambiar (Nambiar 2010 ) argued that food suppliers could use technology to enable continuous monitoring to preserve quality and provide cheaper food products to consumers. The use of technology results in increased operational efficiencies and savings throughout all the links of the food supply chain (Huscroft et al. 2013 ; Jayaraman et al. 2008 ; Jovanovic et al. 1994 ).

Digital technologies are constantly developed and deployed across the agro-food system, from the farmer to the consumer (Rotz et al. 2019 ). Over the past twenty years, advances in information and communication technologies (ICTs) have enabled new opportunities and innovations for improving the outcomes of agricultural activities (Xin and Zazueta 2016 ). For example, Radio Frequency IDentification (RFID) technology can be integrated into the food supply chain allowing organizations to gain enhanced granularity in supply chain traceability for compliance and business process improvement (Attaran and Attaran 2007 ). RFID also enables the real-time monitoring and visibility of re-usable assets such as pallets or totes carrying food products. It facilitates the acquisition of more accurate inventory data and tracking of food cargo at various levels of aggregation in the supply chain. The emergence of the Internet of Things (IoT) enhances the pervasive presence of ‘things’ or ‘objects’ with RFID tags, sensors and actuators interacting or participating on a network (Atzori et al. 2010 ). This can benefit the food industry and improve aspects such as the management of food loss (food loss occurs in pre-consumer phases) and food waste (Wen et al. 2018 ). The use of IoT in food chains has also intensified with billions of ubiquitous and interconnected devices ranging from mobile tools, equipment and machinery on farms to household appliances and temperature-sensing devices (Rao and Clarke 2019 ). When IoT is combined with other technologies, it helps to visualize food supply chain processes and geographic mapping of supply routes (Rejeb 2018a , b ; Rejeb et al. 2019 ). Furthermore, sophisticated tools, devices and technology also include autonomous guided vehicles (AGV), precision farming using robotics and artificial intelligence (AI), distributed ledger technology (DLT), cloud computing and BD tools that combine to reshape agriculture at an unprecedented pace (Phillips et al. 2019 ). Technology is leveraged to process and handle large data streams from multiple sources and origins in the food chain.

BD is perceived to be a critical technology in food chains, agriculture, and other sectors of the economy (Sonka 2014 ). BD is defined as “ a conglomeration of the booming volume of heterogeneous data sets, which is so huge and intricate that processing it becomes difficult, using the existing database management tools ” (Subudhi et al. 2019 , p.2). It can be understood as the processing and analysis of large data sets obtained from various sources such as online user interactions, consumer-generated content, commercial transactions, sensor devices, monitoring systems or any other consumer tracking tools (Li et al. 2019 ). BD also refers to the massive amounts of digital information about human activities, which are generated by a wide range of high-throughput tools and technologies (Marchetti 2016 ). According to Cavanillas et al. ( 2016 ), BD is an emerging field where innovative technology offers new ways of extracting value from the volumes of data and information generated. In the context of food supply chains, BD is a fast-growing area that supports decision-making processes, differentiates and identifies final products based on market demands, and aids in food safety (Armbruster and MacDonell 2014 ). Research and developments on crop improvement and sustainable agriculture have significantly benefitted from the usage of BD in crop modelling for targeting genotypes to different environments (Löffler et al. 2005 ). For instance, analyses based on consumption and crop growth data could aid farmers in determining which crop varieties to plant and which to minimize, enhancing crop yield, increasing sales, and maximizing returns on investment (Tao et al. 2021 ). Similarly, the use of big geospatial data (e.g., from wireless networks, farm machinery telemetry, and periodic remote sensing) enables better management practices in soil erosion, water pollution, and disaster risk management in agriculture (Řezník et al. 2017 ). The ability to collect and analyze data on crop variety, quantity, quality, location, weather events, market prices, and management decisions can support predictive analytics tasks and enable farmers and farming cooperatives to improve crop forecasting (Jakku et al. 2019 ). The use of BD also encourages the development of precision agriculture, which contributes to water conservation (O’Connor et al. 2016 ), soil preservation, limited carbon emissions (Ochoa et al. 2014 ), and optimal productivity (Mayer et al. 2015 ).

Furthermore, the advent of BD has the potential to improve the design of food supply chains, the relationship development among stakeholders, enhance customer service systems, and manage daily value-added operations (Waller et al. 2013 ). The application of BD can help food businesses become more profitable by increasing their operational efficiencies, improving their potential economic gains, and optimizing their resource allocation. When BD is combined with artificial intelligence (AI) tools, the risks related to the occurrences of pathogens, contaminants or adulterants used in economically motivated adulterations (EMA) in the agriculture chain can be predicted (Marvin et al. 2017 ; Spink et al. 2019 ). Although these benefits are tangible, several challenges remain.

While BD has gained remarkable attention from both scholars and practitioners, research investigating the applications of BD in food chains remains scarce (Rotz et al. 2019 ). Moreover, few studies are using BD analytics with a focus on sustainable agriculture and food supply chains (Kamble et al. 2019 ). Therefore, to fill this knowledge gap, the primary goal of this study is to explore the relevant literature and identify how BD potentially impacts food supply chains. By synthesizing the literature published in leading journals, authors strive to demonstrate how the adoption of BD in the food supply chain will improve operational efficiencies, enhance food quality and safety, and develop a sustainable food ecosystem. In dealing with this increasingly important topic, this study aims to provide a deeper understanding of the following research question (RQ):

RQ: What are the possibilities of leveraging BD in the food supply chain?

The contributions of this research to the BD literature is significant. Based on the authors’ current understanding and knowledge, this study presents the first reference to the potential of BD in food supply chains. Besides, the review is among the first to capture the dynamic nature of this topic, providing a systematic review of the recent investigations on BD in the context of food supply chains from literature appearing in leading journals. The review of previous scholarly research provides a timely summary of current evidence that can be used to increase the understanding of BD for scholars focused in the food, technology and supply chain industry. Food industry practitioners and decision-makers can derive new insights into how to design sustainable food supply chains with the emerging field of BD. Thus, this study is motivated by the limited discourse about the usefulness of BD in supply chain management (Engelseth et al. 2018 ). Hence, this gap in the literature is what authors explicitly intend to fill.

The remainder of the paper is structured as follows. Section  2 describes the methodology of the review. The subsequent section presents the statistical classification of publications. Section  4 provides a detailed discussion of the possibilities of BD in the food supply chain based on the findings of the reviewed literature. In Section 5 , some challenges of BD are discussed. The last section concludes the papers, discusses the research contributions, limitations and future research directions.

2 Methodology

2.1 research protocol development.

To answer the research question of the present study, the authors conducted a systematic review of published literature following the guidelines proposed by Denyer and Tranfield ( 2009 ). A systematic literature review (SLR) is a scientific activity that aims to evaluate and interpret all available research relevant to a particular research question or topic area or phenomenon of interest (Kitchenham and Charters 2007 ; Kitchenham 2004 ). An SLR is also a method that helps to consolidate and advance scientific research through locating, appraising and summarizing the existing literature. In order to survey the current state of scientific knowledge regarding the research question, an SLR is driven by prescribed steps to ensure the relevance of the retrieved literature, the minimization of research errors and bias, and the reliability of the quality assessment. The presentation and the process of the SLR in this study aim to establish a familiarity with what is already published about BD applications in the food supply chain. Along the process, care is taken in ensuring that the steps of the review are transparent, rigorous, reliable and repeatable. Furthermore, the authors developed and strictly followed a review protocol that is based on the iterative cycle of identifying adequate search keywords, selecting the relevant studies, and eventually carrying out the analysis. The review protocol is generated based on the central research question and the search string in order to extract the relevant studies. All the authors jointly specified and developed the necessary stages of the protocol. Table 1 describes in detail the selection of the search database, the collection of studies, and the eligibility criteria.

2.2 Data collection

Based on the surveyed Scopus research database, the initial result of the search queries was 131 publications. To further refine the results, the corresponding author undertook the removal of duplicates and the articles with missing bibliographic data points. The publications were also analyzed and filtered according to the eligibility criteria mentioned in Table  1 . The authors screened the titles and abstracts to identify the initial relevant studies, retrieving 62 publications for full-text review. After reading the full content and assessing the quality of articles, a total number of 41 articles were selected for complete review. The final selection of articles was guided by the research question of this study. In other words, out of the 62 publications, authors only considered publications that identified the possibilities of BD from the food chain perspective. As a result, all the 41 publications were relevant to the scope of the present study, and they provided discussions on BD from the perspective of food supply chains. Figure  1 shows the process of data collection.

figure 1

Schematic presentation of data collection

3 Statistical classification of publications

3.1 publications by year, country, and journal, 3.1.1 publications by year.

The search was carried out in October 2019. Figure  2 presents the number of publications published by year and extracted from the execution of the research protocol. Despite being a well-established technology, the interest in BD within the food industry has considerably increased over the recent years. Papers studying the application of BD to food supply chains were almost all published from 2013 onward. More specifically, there is an upward trend in the number of articles published on the subject from the year 2013 to 2019. The number of articles published from 2014 onward has exponentially increased, showing that the applications of BD have gained more recognition and increasing academic attention among food chain researchers. The reason is that many globalized food supply chains are currently migrating to an Industry 4.0 setting, embracing modern technological solutions that are commonly used in other industries (e.g., automotive industry). Industry 4.0 represents a milestone for the modernization and acceleration of food supply chains. As a critical technological component of this emerging paradigm, BD promises a revolutionary leap in the management of food chains among highly dispersed networks of several actors. BD contributes to the successful development of data-driven food supply chains responding to the core needs of businesses and other stakeholders. Out of the total reviewed studies, 36 papers were published in the last three years (2016-2019), reflecting that the integration of BD into food chain activities is still a nascent research area worth discussing and exploring in a much more in-depth manner.

figure 2

Publication details according to year

3.1.2 Publications by country

In order to analyse the geographical distribution of publications concerning BD in the food supply chain, the authors’ affiliations were identified at the time of publication. As shown in Fig.  2 , a significant contribution to the BD literature in the context of food supply chains came primarily from the USA and the UK, with 15 and 7 papers, respectively. This finding is predictable for both countries. For example, Armbruster and MacDonell ( 2014 ) noted that several efforts are steadily underway in the US food system to harness BD to preserve the quality and safety of food products. BD applications in weather and climate have been applied in the USA in the establishment of climate predictions and disaster response in real-time network systems using satellite image data (Lee et al. 2015 ). According to the analytical agency Mind Commerce, the market size of BD in the US in 2013 reached $20 billion, whereas, in 2014, the value was $29, achieving a growth rate of 45% (Ramzaev 2015 ). The importance of BD is also rising in the UK, where the technology has been identified as a driver for economic growth and one of the eight key government priorities (Government 2013 ). The UK government invested £ 73 million to help public and academic projects to unlock the potential of BD in diverse sectors of the economy. Agrimetrics is one of the agricultural innovation centres recently launched in the UK to engage with the food industry stakeholders and enable detailed and collective understanding of the needs of farmers, food producers, retailers, and consumers through the use of BD and analytical tools (Agrimetrics 2015 ). To a lesser extent, scholars from Canada and China were equally responsible for the publication of 4 articles. In this regard, Barrados and Mitchell ( 2017 ) pointed out that there is a proliferation of automated data systems in Canada. This finding is consistent with the assertion of Clarke and Margetts ( 2014 ) who noted that the government of Canada was later than the UK and the US in introducing an open data initiative, which was set up in 2011 by Tony Clement, President of the Treasury Board. Five countries, including India, Japan, Malaysia, South Korea, and Spain, were responsible for ten articles (two each). Only one publication was identified in every remaining country within the sample of the relevant literature.

When authors considered the analysis of publications on a continental basis, researchers from North America are the central contributors to the literature representing 37% of the total participation. To a lesser extent, relevant contributions for each of Europe and Asia represented respectively, 29% and 24% of the total studies. There was an increasing international focus on BD applications to food supply chains that are reflected in the contributions of developing countries in Africa with 8% of the total relevant studies. In comparison, Oceania represents 2% of the total studies. These findings suggest that the rise of BD is not limited to developed economies, but also the technology has extended to the food supply chains of the developing economies (Fig. 3 ).

figure 3

The distribution of publications among countries

3.1.3 Publications by journal

The reputation and credibility of the journal ranking have a significant impact on how people assess the value of the publication. The classification of journals was facilitated by the use of the BibExcel tool. The reviewed publications were from 37 journals. While ranking the journals based on the citation analysis, twenty-nine (29) articles were published in journals that had an impact factor in Journal Citation Report- JCR (2019) . Table  2 presents the journal titles, the number of publications, and the impact factors exceeding 4. The category “ Others ” includes 29 journals, of which only 18 journals have an impact factor. It should be noted that all the publications spanned across a wide range of fields that cover food sciences, manufacturing, computer sciences, supply chain management and logistics, and business. The variety of the scope of the journals reflects the multi-dimensional perspectives of BD and its versatile applications to several areas in the food supply chain.

3.2 Big data publications based on the type of research

Figure  4 presents the distribution of the selected 41 papers by the methodological approach used. Two main research approaches were identified for the classification of articles; conceptual and empirical. Conceptual papers review and discuss the applications, theories, capabilities, and challenges of BD based either on the extant literature or without the collection of primary data. However, empirical papers tend to present data collected through case studies, interviews and focus on measurable and visible BD activities and processes in the food supply chain through other methodological approaches such as algorithmic analyses, prototypes, and system designs. As shown in Figs.  4 , 17 papers provided a conceptual discussion or review on BD. The remaining 24 papers dealt with the topic using empirical research approaches that included case studies and interviews (7), algorithmic and mathematic analyses (4), prototype and system design (4), survey and multi-methods (3). Table  3 presents in detail the classification of these studies according to their methodological approaches.

figure 4

Distribution based on the type of research

Figure  5 shows the trend of how different research approaches have been used to study BD in the context of food supply chains during the period 2013-2019. The trend depicted in Fig.  5 reveals that there is a steep increase in the conceptual and review studies. The trend also shows that the concepts applied to BD research are being tested and validated through empirical techniques and methods such as case studies, interviews, algorithms, prototypes and surveys. While there is a sharp increase in theoretical studies, the increase in studies using empirical investigations is not significant. Therefore, empirical studies are necessary in order to assess the effectiveness and efficiency of BD in the food supply chain.

figure 5

Distribution of research approaches during the period 2013-2019

4 Review discussion

4.1 increased knowledge and insights.

In highly uncertain business environments, the dynamic and globalized nature of the food supply chains has created both fragmentation and complexity with a higher dependency on data and information analysis (Gereffi et al. 2012 ; Kamble et al. 2019 ). Large unstructured data sets are now generated on a real-time basis, which challenges the current approaches for decision-making and calls for a revamped focus on advanced analytical tools (Xin and Zazueta 2016 ). The proliferation of new technologies has given rise to a wave of data originating from different sources such as IoT and wireless sensor networks, the web, mobile applications, and social media. The ability to effectively process these data, manage information and extract knowledge is becoming key for achieving competitive advantage (Curry 2016 ). Advances in information technology offer new possibilities to extract new insights and knowledge from BD (Akhtar et al. 2018 ). The advantage of BD tools compared to conventional analytics and business intelligence is their ability to more effectively process the massive volume of data than others (Subudhi et al.  2019 ; Alfian et al. 2017 ).

In food supply networks, BD enables companies to discover consumers’ needs, create new values, and improve the management of their organizational processes (Ji et al. 2017 ). According to Engelset et al. ( 2019 ), BD is not a pure technology per-se; instead, it is a valuable method and tool set to manage, analyze, capture, search, share, store, transfer, visualize and query supply chain information. In the agriculture field, BD can help to efficiently extract value from the vast amounts of data such as environmental information, biological data, agricultural equipment information, monitoring data of production processes, sales and management data, food safety procedures, yield rates and soil health (Li et al. 2019 ). The high capabilities to process and handle large datasets can optimize the operational decisions and coordination in the food chain. As such, the knowledge gained from the application of BD can be useful in designing adaptive processes for the optimization of the food supply chain. In this context, companies operating in the food industry would be able to optimize process steps from procurement to production to marketing by deriving new insights that were traditionally ‘hidden’ within data patterns (Ji et al. 2017 ). In this regard, Sonka ( 2014 ) argued that BD tools are more efficient in enabling analysts to explore massive quantities of texts and identify the relevant descriptors within the information. BD allows food retailers to adapt and become consumer-centric by providing useful analytical tools necessary for extracting relevant insights into consumer sentiments and behaviours (Singh et al. 2018 ).

In the era of BD, food supply chains are heavily dependent on the use of technology to create valuable knowledge. The mining of the data generated at each echelon of the supply chain provides an effective basis for agri-food decision-making, optimization of processes, and identification of interdependencies (Li et al. 2019 ). For example, a BD platform is needed to handle a large amount of unstructured and continuously generated real-time sensor data (Alfian et al. 2017 ). The time and temperature information retrieved from the sensor network and analyzed with BD tools provide real-time insights into the product shelf-life information (Li and Wang 2017 ) and can help to reduce food waste. The intuitiveness of IoT networks and connected sensors across the food supply chain can be enhanced with BD to capture data related to time and temperature and to share it with exchange partners in order to dynamically manage the optimization of storage, packaging, delivery and selling according to the data drawn from the sensor networks (Li and Wang 2017 ). The increased data visualization capability can be applied in real-time to fresh food supply chains to improve customer value and reduce costs (Engelseth et al. 2019 ). Khanna et al. ( 2018 ) argued that the combination of BD, advanced information and computational technologies could improve knowledge of the processes and relationships in the agri-food sector. Tan et al. ( 2017 ) pointed out that the ability of BD to extract embedded knowledge from large amounts of data can help to solve several specific issues in the halal food industry, such as the contamination of halal food products. Therefore, food businesses, including small and medium enterprises can utilize BD to create actionable knowledge and insights, strengthen their oversight and management of data, and improve their competitiveness in the increasingly competitive global marketplace (O’Connor and Kelly 2017 ). Based on the previous discussion, we develop the following research proposition (RP):

RP1: BD supports food supply chains by increasing knowledge and actionable insights.

4.2 Improved decision-making

According to Malakooti ( 2012 ), decision-making is a complex, multi-dimensional process that can take place spontaneously without any prior planning, or it may emerge after exhaustive and well-contrived analysis. The complexity of supply chain management has resulted in a lengthy decision-making process due to the time required to access information that is necessary to make business decisions. In the context of global food supply chains, strategic decision-making is essential as the holistic efforts could increase the profitability of an entire chain from an efficient framework (Zhong et al. 2017 ). Despite the advances in technology and decision support systems, achieving responsive and adequate decision making is a difficult task. However, leveraging BD in food supply chains can significantly improve decision-making. Moreover, BD counteracts the conventional ways of thinking and decision-making that are based on the intuition and experience of the owner or manager (O’Connor and Kelly 2017 ). BD enables a more informed, evidence-based decision-making (Akhtar et al. 2018 ) by providing managers with access to explicit information and equipping them with new tools and capabilities (Sonka 2014 ). BD provides sophisticated tools where farmers can assess different scenarios from different farming decisions (Xin and Zazueta 2016 ). In this regard, Kamilaris et al. ( 2018 ) developed the AgriBigCAT platform that can support farmers in their decision-making processes and administration planning to meet the challenges of increasing food production at a lower environmental impact. Moreover, BD increases the visualization of information across the food network and drives enhanced transparency, higher productivity, and informed decision making (Ji et al. 2017 ). Decision making would no longer be undertaken in food supply chains with insufficient or fragmented data and information. Consumers also benefit from the outputs of BD initiatives as it can provide contextual information about the food, its origin, method of processing and other information, which aids in a more informed purchasing decision. Lin and Mahalik ( 2019 ) argued that BD improves data storage and enhances the application of agri-food scientific research by providing intelligent decision-making. Tan et al. ( 2017 ) noted that halal industry players could make better and more efficient decisions using BD. Therefore, BD enables food supply chain exchange partners to be involved in interactive and consistent decision processes. BD leads to more intelligent and smarter decision making that can improve the operational performance of food chains, reduce costs, minimize the cycle time of decisions, and mitigate potential risks. Thus, we suggest the following research proposition:

RP2: BD facilitates decision-making processes in the food supply chain.

4.3 Improved efficiencies

Managing efficiencies in food supply chains is an ongoing process that requires the better utilization of available resources, the optimization of processes, and the minimization of costs (Angkiriwang et al. 2014 ). Hence, food supply chains are pressured to enhance efficiencies at every stage, from procurement, logistics, manufacturing, marketing and sales to after-sale services. Similarly, the agri-food sector is dynamic, diverse, and requires more sophisticated tools to improve efficiencies (Duncan et al. 2019 ).

As technology is critical for improving supply chain efficiencies (Attaran 2017 ), the use of BD and its visualization capabilities allows firms to automate the process of exploring hidden patterns that can occur in the food supply chain efficiently and cost-effectively (Ji et al. 2017 ). BD allows food supply chain businesses to explore every opportunity to improve their operational efficiencies, simplify processes, and reduce transaction costs. The management, analysis and response to food-related data can be facilitated through BD and automated to predict situations in real-time (Tzounis et al. 2017 ). For example, Kshetri ( 2017 ) argued that a system based on BD could deliver information to farmers and water service providers on a real-time basis about the current and predicted water and soil moisture levels. Alfian et al. ( 2017 ) proposed a real-time monitoring system that utilizes smartphone-based sensors and BD to handle IoT-generated sensor data and helps food operators to implement critical strategies related to the perishable supply chain. Farmers can capitalize on BD to monitor the health status of animals in the food chain. To confirm this development, Sivamani et al. ( 2018 ) proposed a method based on BD to control the nutritional intake of the livestock, improve the health and diet of animals, and support the early detection of diseases.

While the applications of BD to agriculture dates back to the 1990 s (Carolan and Carolan 2017 ), the technology can play a substantial role in advancing modern precision agriculture. Precision agriculture is a technology-driven approach for the management of farming activities such as the monitoring, estimation and prediction of crop-related data. According to Bucci et al. ( 2018 ) precision agriculture is adopted by innovative farmers who rely on the capabilities of BD to enable the intelligent usage of precision farming data. Similarly, BD is a promising instrument for farmers wishing to develop smart agriculture, improve their productivity, and enhance their integration in the food supply chain. The constellation of technologies in the agri-food sector, such as remote sensing, satellite imagery and high-spatial-resolution BD from farms, has already produced a sophisticated method of farming that increases the efficiency of agricultural production and enables site-specific crop and livestock management decisions (Khanna et al. 2018 ). In this respect, Li and Mahalik ( 2019 ) posit that BD can utilize data from GPS/GIS to track crop yields, determine the optimization of crops, and increase harvesting productivity. The combination of BD with IoT data can help farmers optimize their farm operation. In research by Kamilaris et al. ( 2018 ), BD is used in an online software platform to analyze geophysical information from various sources, estimate the impact of livestock on the environment, and increase resource efficiencies. Khanna et al. ( 2018 ) noted that in 2017, Great Lakes Watershed Management System brought environmental forecasting capability to precision agriculture by allowing farmers to input GIS coordinates for their fields, run tillage and fertilizer management scenarios, and to view predicted estimates of nutrient loading and soil erosion to nearby water bodies. Therefore, the enormous potential of BD applications to enhance precision agriculture is evident in the reviewed papers. Therefore, BD aids in the efficient usage of scarce resources (e.g. water) and the optimization of crop cultivation and harvesting. Furthermore, BD helps to develop more accurate models for agriculture management and monitoring of farming activities. Consequently, we introduce the following research proposition:

RP3: BD has a positive impact on the operational efficiencies of food supply chains.

4.4 Reliable forecasting

Food supply chains are inherently complex to the extent that inputs cannot be completely controlled, managed, and safeguarded against uncertainties. Therefore, forecasting is a necessary activity that aims to evaluate the value of events in the future with uncertainty based on the observed patterns from the previous record (Ahmed 2004 ). Demand forecasting has long been a critical issue of the food industry that calls for reconsidering sophisticated technologies such as BD to aid more accurate and useful forecasting (Nita 2015 ). Hence, BD can act as a critical enabler in the food supply chain because of its power to aid forecasting accuracy and precision. The predictive capabilities of BD are beneficial to support the management of food chains, which are increasingly characterized by their short life cycles and speed of response. Moreover, the technology enables the systematization of demand forecasting, resulting in improved accuracy of consumers’ demands, reduced distribution costs and disposal losses (Nita 2015 ). Farm management and operations will dramatically change because of the high resolution of BD information, real-time forecasting, and transparent prediction models. In crop management, Badr et al. ( 2016 ) noted that BD could provide the data required to run crop models under different climate and management scenarios, and this approach is useful for mitigating some food security issues. The authors argued further that technology and BD-centric forecasting could support decision-makers, crop growers, and researchers to gain a deeper understanding, better manage supply and demand of the food chain, anticipate food-related challenges, and develop practical solutions to overcome food insecurity and price uncertainties. Testing the credibility of forecasting results, Nita ( 2015 ) found that a BD-enabled system for a food manufacturer could produce a high forecasting accuracy within 70% of the target commodities. The benefits of proper and reliable forecasting include the optimization of food chain operations, lower product perishability, better planning and utilization of resources, and the improvement of the overall supply chain performance. BD also drives more collaborative forecasting and scheduling between the food business and its supply chain exchange partners, resulting in better inter-organizational collaborations. Thus, the following research proposition emerges:

RP4: BD leads to more accurate and reliable forecasting in the food supply chain.

4.5 Waste minimization

In the context of agri-food supply chains, waste represents a catch-all term that encompasses non-value-adding activities, excess inventories, additional wait times, unnecessary processing steps, and other variabilities. According to Hicks et al. ( 2004 ), waste is a strategic issue in the supply chain that forces companies to seek ways to minimize all types of waste and thus achieve cost-savings. Research on food waste has established that one-third of the food produced is either wasted or is lost, accounting for 1.3 billion tons per year (Mishra and Singh 2018 ). (Note, food loss refers to pre-consumption stages such as pre and post-harvest loss whereas food waste occurs when the food is consumable but discarded).

Supply chain waste may stem from ineffective quality or process control, and large quantities of inventories can perish in agri-food supply chains. As the minimization of resource waste is a topic of paramount importance in the food supply chain, there is a high potential for BD tools to reduce waste in the food supply chain (Mishra and Singh 2018 ). The minimization of food waste through BD can result in increased resource utilization, better profitability and reduced risk of food insecurity. The visualization capabilities of BD can enhance the traceability of food supply chains and the visibility of key business processes. Belaud et al. ( 2019 ) pointed out that BD leads to more sustainable food supply chain designs that valorize agricultural waste. Li and Wang ( 2017 ) developed a BD-based system that aggregates time when the temperature exceeds a certain threshold at each stage of a supply chain and estimates the impact of improper quality control and perishability of food products (e.g., reduction in shelf-life, risk of spoilage). This increased control enables retailers and manufacturers to deliver satisfactory food quality and overcome the severe financial consequences of food loss and waste in the supply chain. Another benefit of BD tools is transparency, in the sense that whenever products pass through the supply chain, effective waste-related decisions can be dynamically made, such as pricing of food products based on their current shelf-life (Li and Wang 2017 ). The possibility of uncovering hidden and valuable insights with BD can also help food chain actors to reduce overall waste. For example, retailers today are utilizing BD for waste reduction by using consumer complaints made in retail stores (Mishra and Singh 2018 ). Data captured from social media (e.g., Twitter) can be analyzed using BD in order to develop effective waste minimization policies in the food chain. Therefore, BD contributes to more sustainable food chains as it can dramatically reduce the occurrence of perishability in the food chain and the immense food loss and food waste. Beyond overcoming the economic losses of waste, the technology also helps to incorporate other sustainability considerations that are relevant to food safety. For example, the aggregation of food data in a BD system empowers the trace-back and track-forward capabilities of the business. Hence, this capability enables the reduction of unnecessary food waste and the fast detection of products involved in foodborne illness outbreaks, their sources, and their current locations (if still in the supply chain). As a result, we outline the following research proposition:

RP5: BD reduces waste in the food supply chain.

4.6 Food safety

Food safety represents a growing and critically important public health issue (Aung and Chang 2014 ). It is a joint responsibility of all actors involved in the food industry to ensure that food is safe to consume. With the increasing concerns and awareness of consumers toward food safety, food supply chain partners are obligated to secure and protect food products from any sort of contamination or adulteration, whether it be unintentional or intentional. The assurance of food safety means that food is safe from causing harm (Demartini et al. 2018 ). To maintain food safety, the use of technology and information systems can provide incentives and accountability measures that are critical for identifying best manufacturing practices for food operators at various stages in the food supply chain (Ahearn et al. 2016 ). In this regard, Marvin et al. ( 2017 ) confirmed the significant role of BD in predicting the presence of pathogens or contaminants by matching the information on environmental factors with pathogen growth or hazard occurrence. Zhang et al. ( 2013 ) developed algorithms that used BD and visualized images to model contamination conditions in an IoT-based food supply chain, helping to develop consumer confidence in the food ecosystem. To assist farmers in the selection of the most eco-friendly beef cattle supplier, Singh et al. ( 2018 ) proposed a BD cloud-computing framework for carbon minimization. The captured information related to carbon footprint can be used by abattoir and processors in their supplier selection decisions while accommodating carbon footprint emissions in this process. Moreover, the deployment of BD in combination with ERP, IoT and other data sources connected to logistics providers can facilitate enhanced product tracking and risk management of food. By providing real-time information about the product, its condition (e.g., temperature), destination routes, including traffic and weather patterns, BD may prove valuable for trend detection of potential contamination during the delivery of food products (Tan et al. 2017 ). As stated earlier, the increased transparency gained from the BD application can provide thorough and real-time monitoring of the quality of perishable food products. In the highly complex global food chain, BD enables supply chain exchange partners to establish more effective and cooperative relationships in order to maintain food safety and enhance transparency. Li and Wang ( 2017 ) outlined that with BD applications, consumers would be able to obtain more information about the product shelf-life variation over time. Access to a granular level of information creates a conducive environment that not only assures food safety but it establishes more trust, confidence and commitment. Such digital transformation, is, according to Li and Wang ( 2017 ), a suitable framework for strategic innovation for marketing, quality management, and supply chain optimization. Therefore, BD can be viewed as a critical and value-adding element for food safety management that can respond to consumers’ growing concerns about food quality and safety. Based on the previous discussion, we suggest the following research proposition:

RP6: BD improves food safety management across the supply chain.

5 Further challenges of big data

The application of BD has tremendous potential in food supply chains. To achieve competitiveness, the food and restaurant industry could embrace BD to derive actionable business insights, make evidence-based decisions (Coble et al. 2018 ; Lokers et al. 2016 ), optimize operational efficiencies, produce reliable forecasts, minimize food waste, and ensure food quality and safety. In their study, Ma et al. ( 2018 ) argued that BD could enable restaurant owners to predict future visitors. For the service-oriented food industry, the implementation of BD has become a necessity given the ability of the technology to provide insights into customer spending habits and support restaurants to more accurately grasp the market trend (Tai et al. 2020 ). Although the benefits of BD for food supply chain players, including those operating in the foodservice industry, are tangible, several challenges are still hampering its wide-scale implementation.

5.1 Data complexity

According to Waldherr et al. ( 2017 ), the challenges of BD stem mainly from the growing amounts of data, the high speed of data generation, and the diversity of data formats and structures. The BD ecosystem is characterized by a great variety of data sources and the velocity of data flows for which advanced computational methods are imperative to analyze data (Zhou 2019 ). Similarly, the need for these methods and techniques is pressing as they allow to manage knowledge of chemical components of foods of importance to human health (Tao et al. 2018 ). Moreover, the increasing interconnectedness and complexity of BD result in overlaps, various links of data, and growing noise. To purify BD, food businesses are required to devise new strategies, tools and technologies that can improve data quality and analysis. In BD applications, poor data quality or so-called “dirty data” (Li et al. 2019 ) could increase concerns over the reliability and validity of BD analyses and create additional costs for food firms. For example, analysts approximate that the cost of poor data quality within a typical business is between 8% and 12% of revenues (Sethuraman 2012 ). Therefore, subtracting noise from BD is a challenging task because data keeps on varying inconsistently concerning time, thereby affecting the mechanism of effective data management (Subudhi et al.  2019 ).

5.2 Security and privacy issues

The BD-driven food supply chains bring enormous challenges for food businesses, especially during data collection, storage, visualization, and information sharing. For instance, these include issues about data security and privacy (Sharma et al. 2018 ,  2020 ). As per Duncan et al. ( 2019 ), cybersecurity threats are problematic in the BD era because of inappropriate access to BD systems, data, or analytical technologies and the nefarious use of information for fraudulent food activities. Food supply chain partners need to secure the public and private information of individuals and businesses, including physical and digital footprints, searches, transaction histories, audio and video communications, service registrations, conversations, and messages (Li et al. 2019 ). The BD ecosystem is fraught with data security risks, which necessitate being carefully evaluated before food businesses engage in the adoption of BD systems. Thus, to sharpen their competitive advantage, food businesses have to ensure a high level of data security to implement BD successfully. Furthermore, the aggregation of data from different and distant information sources has also raised several privacy concerns due to the so-called private information leakage (Guo and Wang 2019 ). As a result, BD systems might entail collecting consumers’ private information without consideration of regulations, laws, and existing standards. Therefore, consumer-privacy issues could deter food businesses from shifting towards BD-enabled food supply chains.

5.3 Organizational challenges

At the organizational level, the lack of necessary capabilities and resources might hinder the applications of BD in the food industry. In this context, Kshetri ( 2017 ) points out that organizations might be in shortage of BD engineers and scientists who can understand, interpret, and perform analytics. This critique is also highlighted in the study of Tan et al. ( 2017 ) who argue that the halal industry still encounters the lack of talented professionals who could work with BD tools and techniques. Besides the need for analytical and technical know-how, organizations might commit sizeable initial investment to implement BD systems (Sonka 2014 ). For resource-constrained food businesses, BD might not be an economically feasible solution since the incorporation of IoT-based systems, and the expansion of human resources through BD corporate training programs could be a costly and risky investment. BD applications in food services can be unaffordable and almost exclusively developed for larger food firms. Therefore, when seeking to invest in BD applications, incapacitated food industry stakeholders, including farmers and foodservice organizations, could be skeptical of the benefits of BD for their business processes and reluctant to integrate BD systems into their organizational structure. This uncertainty could be further aggravated by the lack of interoperability (Jeppesen et al. 2018 ) among the technologies leveraged in the food supply chain.

6 Conclusions

This study aims to investigate the current state of research on the applications of BD to food supply chains by conducting an SLR on all relevant studies through an appropriate review methodology. Forty-one (41) articles were thoroughly examined and analyzed for this purpose. The findings of this SLR showed that the application of BD to food supply chains is getting increasingly popular with an increase in the number of publications recently. Initially, the SLR was focused on identifying the type of methodologies that were used in the reviewed publications. The use of conceptual approaches to contextualize and extend discussions on the possibilities of BD in the food chain was frequently noticed. Empirical methodologies were employed to demonstrate and validate the effectiveness of BD in sustaining food supply chains from different aspects. A significant number of studies (n= 13) used a case study methodology and interviews to gather data. Some studies developed and proposed prototypes, applied surveys or created system designs to validate the benefits of BD to food manufacturers, retailers, and consumers. The enablers of BD in the food industry identified from the SLR contribute to the literature, concepts, and theories on the capabilities of BD in bringing effective solutions to the management of food chains. In many instances, the ability to extract useful knowledge and insights from data demonstrates the enormous potential of BD and is frequently reported in the majority of studies. However, an observed lack of research studies investigating the capabilities of BD in optimizing food processes and supporting food procurement, processing and marketing is identified. It can be a potential area for further research.

The theoretical findings reveal that previous research on the application of BD to food supply chains have focused primarily on providing the basic concepts of BD and use cases demonstrating its benefits. A paucity of studies synthesizing the advantages of BD was found in the literature review. Hence, this study fills a knowledge gap and presents a contribution to the literature in the form of a detailed SLR. The findings of the SLR revealed six key enablers of BD in the food supply chain namely;

Improved knowledge and predictive insights

Decision-making support

Enhanced efficiencies

More accurate forecasting

Process-based waste minimization

Food safety management

The findings of the review revealed that BD implementations could be impeded by the poor data quality, security and privacy concerns, lack of organizational capabilities and skills, high initial investment costs, and resistance to operate with BD systems. Thus, future research studies may investigate the solutions necessary to accelerate the uptake of BD in the food industry. Research in this direction will help to provide a more balanced understanding of what enables and hinders the development of BD-based food supply chains. Further, this study identifies that BD can be combined with other technological tools such as IoT, AI, cloud computing, and decision support systems (DSS) to substantiate the value of technology in the agri-food industry. Scholars may investigate to what extent food businesses can benefit from the integration of these technologies in the supply chain. The findings of the SLR are one of the initial attempts to contribute to the understanding of BD applications and its connection to the food research area. The utilization of BD could unlock several benefits and sustain the delivery of safer food products to consumers. Therefore, food industry practitioners and decision-makers would gain a deeper understanding of the promising role of BD in contributing to the evolution of sustainable activities in their organizations. The enablers of BD identified in this study may be considered in the formulation of guidelines necessary for BD implementations in the food chain.

Although this study provides a timely review of an increasingly emerging technological capability, we recognize several limitations. The use of Scopus as a comprehensive database does not guarantee the full coverage of the extant literature. Some articles outside of Scopus might be relevant to the scope of the study but have not been considered. Hence, we encourage the replication of review studies in the future and the use of other accessible databases such as Web of Science and Google Scholar. The findings of this study are also limited to the selected number of publications, and therefore, the theoretical inferences drawn here should be validated with other empirical research methods such as expert interviews.

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Rejeb, A., Keogh, J.G. & Rejeb, K. Big data in the food supply chain: a literature review. J. of Data, Inf. and Manag. 4 , 33–47 (2022). https://doi.org/10.1007/s42488-021-00064-0

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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.


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.


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.

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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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Application Research: Big Data in Food Industry

1 Key Laboratory of Aerospace Information Security and Trusted Computing, Ministry of Education, School of Cyber Science and Engineering, Wuhan University, Wuhan 430072, China; nc.ude.uhw@71oatq (Q.T.); nc.ude.uhw@gnidwh (H.D.)

Hongwei Ding

Huixia wang.

2 Hubei Provincial Institute for Food Supervision and Test, Wuhan 430223, China; moc.621@790xhw

Xiaohui Cui

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A huge amount of data is being produced in the food industry, but the application of big data—regulatory, food enterprise, and food-related media data—is still in its infancy. Each data source has the potential to develop the food industry, and big data has broad application prospects in areas like social co-governance, exploit of consumption markets, quantitative production, new dishes, take-out services, precise nutrition and health management. However, there are urgent problems in technology, health and sustainable development that need to be solved to enable the application of big data to the food industry.

1. Introduction

Consumers are no longer satisfied with having enough to eat; food quality has become a key factor in determining consumer choice [ 1 ], and their demands and preferences change with the season, time, weather, mood, and other factors [ 2 ]. However, food choice is a luxury not every person enjoys. The Food and Agriculture Organization (FAO) of the United Nations reported that 88% of countries face a serious malnutrition burden and so has issued healthy dietary guidelines that cover a wide range of food and nutrition ( http://www.fao.org/nutrition/education/food-dietary-guidelines/regions/countries/united-states-of-america/en/ (accessed on 2 September 2021)). Traditional food science has been unable to satisfy increasing demand for food in a world where “healthy nutrition” has overtaken “well-fed” as the predominant paradigm of consumption ( https://baijiahao.baidu.com/s?id=1681293145359468742&wfr=spider&for=pc (accessed on 2 September 2021)) [ 3 ]. However, big data offers food science a new means of scientific analysis [ 4 ].

The food supply chain is composed of economic stakeholders from primary producers to consumers. It has the characteristics of large volume, many links, wide distribution, diverse types, and scattered data, and it is becoming more complex. Millions of tons of food move around the world every year, so no enterprise can promise that every risk node on the production line is absolutely safe. Any flaw in the supply chain could bring a disaster and huge regulatory difficulties for government departments. However, big data provides a solution to regulatory difficulties [ 5 ] by helping enterprises understand consumer demand better and uncover food industry trends through big data analysis. The food industry collects large datasets through real-time monitoring and can improve food safety if analyzed in conjunction with sample data [ 6 ]. When industry data is combined with data on consumer dietary behavior, food enterprises can optimize their investment and adjust the direction of research and development in a timely manner. [ 7 ].

This paper uses bibliometrics to analyze the research progress of big data in the food field. According to Bradford’s Law, a small number of core journals collect enough information to reflect the latest and most important advances in science and technology. The database of Web of Science Core Collection contains more than 12,000 core journals from more than 250 subject areas. It defined the search topic “Food & Big Data” and selected 1672 papers from its database. The research progress of big data on food is shown in Figure 1 It has increased significantly since 2014 because USD 35.8 billion was invested in global agrifood from 2010 to 2019, and after 2014 the scale of financing grown rapidly ( https://agfunder.com/research/agfunder-agrifood-tech-investing-report-2019/ (accessed on 2 September 2021)). This increased capital investment promoted research into big food data, and the rapidly rising trend is from 2010 to 2021 is shown in Figure 2 . China’s food industry has attracted global attention since 2012 because the country’s new government leaders stressed that they would pay more attention to food safety ( http://www.xinhuanet.com/politics/2017-01/03/c_1120239001.htm (accessed on 2 September 2021)). As the second largest economy and the world’s largest trading country, China has great international influence. ( https://www.brookings.edu/research/chinas-influence-on-the-global-middle-class/ (accessed on 2 September 2021)). Big data has been one of the focuses of research since 2013, mainly in food safety, food security and agriculture. Its application to food safety may still be in its infancy, but it is affecting the entire supply chain. The literature contains analyses on the feasibility and need for big data in the food industry [ 4 , 6 ], but there are no in-depth analyses. Therefore, this paper will mainly discuss the following three aspects in depth.

An external file that holds a picture, illustration, etc.
Object name is foods-10-02203-g001.jpg

Research progress of data in the food field. From 1990 to 2010, the research papers of big data on food grew at a rate of 100% every five years, and since 2010 it has grown by nearly 300% every five years.

An external file that holds a picture, illustration, etc.
Object name is foods-10-02203-g002.jpg

Analysis of the research direction of the data from 2010 to 2021 in the food field. Since 2013, food security and big data have become a focus of researchers interested in the potential value of big data on food. The research focuses on IoT-based data collection and its application to smart farming, supply chain management, food nutrition, and sustainable development.

  • The major sources of big data in food industry and its challenges.
  • The market application trends of big data in the food industry.
  • The main challenges to applying big data.

The authors of this paper hope to help researchers develop a deeper understanding of the research progress of big data in the food field and to provide guidance for further research.

2. Big Data in Food Industry

Big data sources of food mainly include regulatory, food enterprise (including data generated at every link of the industrial chain from planting to restaurants), and media data (including food-related news, video, pictures and audio). High-quality big data analysis can help develop the food industry, whereas analyses from low-quality data can adversely affect managers’ prediction of market demand [ 8 ], and social stability [ 9 ].

2.1. Food Regulatory Data

Food regulatory data usually includes department regulatory and product sampling data. Marvin [ 4 ] has detailed public information about food safety supervision and sampling inspection in various countries. This information includes, reports on animal and plant disease monitoring, hazards, food-borne diseases, which provided support for researchers of deep-risk information. Rapid Alert System of Food and Feed (RASFF) is a commonly used online food safety database for industry and scientific research in the European Union (EU). Food safety databases in other countries include the Import Rejection Report (IRR) and the Inspection Classification Database (ICD) in the U.S. and the State Administration for Market Regulation (SAMR) alerts in China [ 5 ]. With the increasingly close connection between countries, the trend of “table globalization” has become increasingly prominent. In 2017, the amount of food China imported from Australia, the United States, Japan, Germany, Southeast Asia, and other countries exceeded RMB 1.5 trillion ( https://www.askci.com/news/chanye/20171212/084457113784.shtml (accessed on 2 May 2021)). As shown in Table 1 , the SAMR usually shares its sampling inspection results of imported and exported food on government websites, which allows consumers to know the quality of food on the market. The U.S. government shares food sample analysis reports through the FSIS system. The EFSA database contains data on food consumption habits and patterns across the European Union. Such statistical data allows users to quickly screen long-term and acute exposures to potentially hazardous substances in the food chain. In addition, the World Health Organization (WHO) established the Global Environmental Monitoring System (GEMS/Food) in 1976, in which participating institutions submit data on food pollutant concentrations and set up data centers to help governments, the Codex Alimentarius Commission(CAC) and other institutions to assess trends in food contaminants [ 10 ]. In 2015, the WHO integrated data from the fields of agriculture, food, public health and economics to build a big data services platform for food safety ( https://www.who.int/foodsafety/foscollab/en/ (accessed on 2 May 2021)) to improve risk monitoring.

The public regulatory database.

DatabaseDatabase TypeData DescriptionCountryOrganizationLink/Source
Import and export
food sampling
Alerts/notificationsResults of
food sampling
Food sampling reportAlerts/notificationsFood sampling reportUSAFSIS
European Food
Consumption Database
Alerts/notificationsEuropean food
consumption habits
Import food samplingAlerts/notificationsResults of
food sampling

Challenges. The sharing and circulation of data among food regulatory departments is conducive to the construction of intelligent supervision of the food supply chain [ 11 ]. However, there are several challenges.

  • Limited shared data. Government departments have not been able to fully disclose detailed monitoring and sampling data, leading to the repeated testing of product quality indicators by enterprises, which has resulted in serious waste of social resources and increased operating costs. How to encourage various departments to share data is an important research direction.
  • Lack of system standards. Due to a lack of system standards, the independence of departments leads to the relative independence of food supervision system. In addition, the limitation of responsibilities further intensifies the independence of departments. So, it is urgent that a new mode of interdepartmental data sharing be explored.
  • Due to inconsist food standards, there are differences in the names and categories of the same food , which is an obstacle to data sharing. The Estonian government has proposed X-Road architecture of data sharing among the basic sectors [ 12 ], and a few European governments will also be involved in international data sharing [ 13 ].

2.2. Food Enterprise Data

The food industry chain is composed of enterprises from agriculture, fishing, processing and restaurant and is characterized by many links and wide distribution. At present, all agricultural machinery is electronically controlled to improve operational performance [ 14 , 15 ]. Cloud computing, the Internet of Things, big data and blockchain can integrate isolated production lines in the food supply chain into data-driven interconnected intelligent systems. Through semantic active technology, each operation is automatically integrated, improving the efficiency of precision agriculture and enterprise management [ 16 ]. Using sensors and drones to collect data on weather, geography, and animal and crop behavior can help farmers optimize crop planting and animal growth cycles. Intelligent devices capture actionable data and make decisions that reduce equipment downtime [ 17 ].

In recent years, research on the IoT in the food industry has promoted the diversification of the IoT platform to address market needs, [ 18 , 19 ] different monitoring models [ 20 ], and unbalanced energy consumption [ 21 ]. IoT-integrated applications will help food companies create new data sources. Industry 4.0 not only promotes the rapid development of Agriculture 4.0, but also enables enterprises to transmit real-time information to identify and meet the changing demands of stakeholders [ 15 ]. According to the Eurostat report ( https://www.brookings.edu/research/chinas-influence-on-the-global-middle-class/ (accessed on 2 September 2021)), the application of smart agriculture will save 4–6% of agricultural costs and increase market value by 3% by 2026. The application of big data can not only enable businesses to deal with challenges in food production, but also to obtain more affordable raw materials to reduce production costs [ 14 ]. It also promotes the development of smart agriculture, which helps save water [ 22 ], preserve soil, limit carbon emissions [ 23 ] and improveproductivity [ 24 ]. Smart agriculture provides an opportunity for farmers, service providers, government and other stakeholders (such as financial institutions, investors, traders) to share their experiences in optimizing the agricultural supply chain with the production sustainability [ 25 ].

Challenges. The food industry can benefit from big data services, but there are challenges that need to be addressed, including data fairness such as the searchability, accessibility, interoperability, and reusability of shared data, and a lack of information standards and data processing technology.

  • Lack of Information standards. (a) The lack of standardized protocols has created incompatibilities among information management systems [ 26 ]; (b) The development of the IoT is still in its initial stages. As IoT manufacturers develop independently, the data generated may be difficult to interpret and share. In addition, there are other problems such as IoT security in food safety. Any insecure IoT node in the food supply chain can be a weak link in the entire system.
  • Immature processing of food big data. Although cloud computing has been used by many organizations, its appplication to big data regarding food safety is still in its infancy. There as also problems such as system scalability, data fairness, data security, and legal issues, which have not been adequately addressed. Blockchain technology is expected to bring a safer and more transparent food supply chain, but it is still immature and difficult to apply. Currently, the blockchain applicationa to food safety is limited to traceability, and issues such as data integrity and data governance still need research.
  • Improved supply-chain decision-making. There is still a need to help farmers make effective decisions in Agriculture 4.0, maintain effective connections among different complex networks, and identifythe dynamic needs of stakeholders.

2.3. Media Data

Social media has become the main way for users to obtain and share food information [ 27 , 28 ]. According to a statistical report on China’s Internet Network Information Center, the number of Internet users in China has reached 854 million, and 88.8% [ 29 ]. Social networks have gradually become the mainstream platform for disseminating information, a constant strem of videos, news, and other types of data [ 30 ]. Purcell et al. [ 31 ] found that two-thirds of Internet users get their news from Facebook and share news through social media. Through research into the generation, and promotion of social events on the Internet, the mode and characteristics of information transmission can be discovered, which provides support for practical application scenarios. In 2009, Google successfully predicted the spread of the H1N1 virus based on query data in its search engine and brought the public valuable time to prevent an outbreak [ 32 ]. Combining the real-time advantage of big data with the conventional and available advantages of traditional data will enable effective response to the transmission of public health events such as COVID-19 [ 33 ]. Singh et al. discovered supply chain management problems by using Twitter data to improve supply chain management in food industry [ 34 ].

The public participates in the discussion of events by different media, and it expresses clear opinions and attitudes in the form of public opinion [ 35 ]. The report ( https://baijiahao.baidu.com/s?id=1617643364060321280&wfr=spider&for=pc (accessed on 2 September 2021)) shows that food safety and food rumors were first among hot food events in 2018, and the topic has become one of the prime targerts for media rumors, and social media’s intensification of rumors can create a widespread crisis [ 35 ]. By analyzing and understanding the trend of food incidents based on social media data, regulators can formulate timely countermeasures like enhancing public awareness through science education and shaping public opinion [ 36 , 37 , 38 ]. However, the field of media data has its challenges that need to be overcome.

  • Multi-source heterogeneous data fusion. Media data on Twitter, Facebook, YouTube and various information portals have complex formats and multiple sources, and there is a lack of technology to identify relevant data in one sources and link it to others. In the absence of a "fusion technology" for multi-source heterogeneous data further study is urgently needed to address social media rumors and their negative impact on public security.
  • Rumor detection. Network rumors seriously impair the public’s ability to recognize authentic of network information. The generation, influence, and propagation mechanism of network rumors have been studied [ 35 , 36 , 37 , 38 ], but there is still no answer to the problem of improving the public’s abiity to evaluate network information. The ability to perceive a risk has a positive impact on users’ attitudes, but social media undermines it. Authoritative information on refuting rumors, such as government notices and mainstream media news, have a significant effect on reducing the public’s acceptance of network rumors and improving the willingness to identify network rumors.
  • Rumor control. Many studies based on communication science and psychology analyze food rumors by the way they are transmitted, but there is a lack of relevant research on government management. Existing food rumors detection technology is mostly simulation, which rarely considers the responsibilities of government institutions. It will be necessary to consider the role of the government, social media, and human networks to establish an efficient, and authoritative information platform to dispel rumors in time. This will improve public risk and prevention awareness, strengthen and punishment mechanisms for rumormongers, and strengthen legal education and behavioral guidance for the public [ 35 ]. The study of social network interaction patterns will be used to explore how to promote and change the public’s perception, attitude, and behavior on rumors concerning food, health or other fields.

3. Application of Big Data in Food Industry

The food supply chain is from farm to table, where the main links are planting and breeding, storage, processing, circulation (transportation) and consumption [ 39 ]. The discovery of value information on original data needs to go through a continuous cycle: of “discrete data—integrated data—knowledge understanding—mechanism extraction—application effect analysis”, from which the potential value of data sets can be mined. The processing system of big data application in food industry is shown in Figure 3 . It is composed of five modules: big data collection of food industry, big data processing and fusion, big data mining and analysis, big data view and big data security. Each module is closely connected, and its functions are briefly described as follows.

An external file that holds a picture, illustration, etc.
Object name is foods-10-02203-g003.jpg

The processing model of big data in food.

  • Big data collection of the food industry. Based on sensors, web crawlers, near-infrared detection instruments, food-related data is collected from different sources.
  • Multi-source data processing and fusion. The collected data contaiins a considerable anount of redundant and dirty data [ 40 ], but through cleaning and conversion they can be removd and the data can be put into a standardized format. Then data features are extracted, and fusion is performed using probability statistics [ 41 ], logical reasoning and machine learning [ 42 ].
  • Big data mining and analysis. This is a discovery mode of mining valuable knowledge from massive data that can accuratetly predict activities through scaled data [ 43 ]. Some big data technology, including SVM(Support Vector Machine) [ 44 ], Random Forest [ 45 ] and Naive Bayes [ 46 ], are used to analyze fused dataset to discover potential patterns to create social value [ 47 , 48 ].
  • Big data view. Due to the characteristics of the complexity and multidimensional of data, it is necessary to generate a data view that can be easily expressed and understood by users. These are usually parallel coordinate, scatter graph, and scatter graph matrix methods [ 49 , 50 ].
  • Big data security. In the lifecycle of big data on food, there are security risks in each processing stage [ 51 ], so research into big data security technology has become an important research topic. This module provides security technical support for all big data processing to ensure the safety, reliability, and controllability of data.

3.1. Social Co-Governance in the Food Industry

Social co-governance in the food industry provides a feasible solution to the issues of food security and food quality by using public wisdom [ 52 ]. Social co-governance is usually based on crowdsourcing to cooperate with consumers or experts to create value [ 53 ]. The failure rate of new food product development exceeds 40%, and the failure of new products usually affects the continued operation of small and medium-sized enterprises [ 3 ]. Large food companies have tried to collect consumer preference data through crowdsourcing, and have decided the direction of product development based on an analysis of big data. The Danone company encouraged consumers to vote for a creamy dessert flavor, and the 400,000 participants in 2006 more than doubled to 900,000 in 2011. Lay’s used the wisdom of crowds to develop more than 245,825 flavors of potato chip [ 54 ]. Procter & Gamble, Starbucks and Unilever sought better product design based on collective intelligence [ 55 ]. Employees often have a wealth of heterogeneous expertise, and companies can gain insight from their workforce to help improve economic performance. In addition, crowdfunding is another form of social co-governance, sharing business risks and alleviating capital pressure through mutual assistance [ 56 ]. Social co-governance has great potential for food security. Combining the mobile data of consumer groups with food shelf life, the intelligent control of food inventory can be realized to prevent food spoilage and waste [ 57 ]. Social co-governance can also be applied to monitoring foodborne diseases [ 32 ], identfying contaminated products, reducing the risk rate of food rumors and enhancing food safety [ 58 ].

Although social co-governance can enable food enterprises to obtain consumer demand information through diversified channels, it is still difficult to obtain effective information in time due to the limitation of enterprise resources [ 53 ]. In addition, there is a lack of an incentive or fair evaluation method in the food industry to convince consumers to participate.

3.2. Exploit Consumption Markets

There is a huge amount of food-related data both inside and outside the food supply chain, and the collection and analysis can promote enterprises to expand their markets [ 59 ]: (1) By collecting commodity and retail information for analysis, they can appraise the market situation, grasp the business dynamics of their competitors, and define the market positioning of products, thereby grasping market opportunity. (2) Collecting consumer information (purchase lists and channels, commodity preferences, usage cycle, family information, working condition, values) will establish a customer database that can give enterprises portraits of their customers that reveal their preferences, consumption tendency, value orientation and commodity reputation. With this information, enterprises can develop efficient marketing strategies and develop trust, so they can continue to compete effectively. (3) Data clustering analysis of consumers’ food evaluations (advantages and disadvantages, quality, nutritional value) from social platforms such as Facebook, Twitter and Sina Weibo, allows enterprises to anticipate potential problems and optimize the quality of goods and services.

3.3. Quantitative Production

By predicting future commercial demand based on historical sales’ data, agricultural and livestock products can be planned to reduce the probability of “cheap vegetables hurting farmers”. In addition, big data analysis can help predict weather more accurately, helping farmers and herdsmen to prepare for natural disasters. Analyses based on consumption and crop growth data help farmers decide which crops varieties to increase and which to reduce, improve crop yield, facilitate rapid sales, and achieve a return on capital. Big data can also optimize grazing area for local herdsmen and improve the usage rate of pasture. Fishermen can scientifically arrange fishing moratoria and locate fishing areas based on the results of big data analysis.

Consumption trends and habits provided by big data, enable governments to provide accurate guidance for agricultural and animal husbandry production, suggest production levels according to demand, and avoid unnecessary waste of resources and social wealth caused by excess capacity. When combined with drones, big data can promote the development of precision agriculture by allowing farmers to collect information on the growth of crops, diseases, and pests, at a much lower cost and with much higher accuracy than by hired aircraft [ 60 ].

3.4. New Dishes New Experience

In 1992, chefs Heston Blumenthal and Francois Benzi were deciding which ingredients with similar flavors would work well together, when someone created a combination of white chocolate and caviar. Due to the chemical differences, it tasted terrible. Today, because of food sciecne, there is a large amount of information about food chemicals [ 61 ] and how they make food taste. Consequently, Ahn et al. [ 62 , 63 ] developed a flavor network of ingredients connected by shared flavor compounds, in which flavors were limited by the type of raw materials. Garg et al. developed a flavor database with richer food materials [ 64 ]. Simas et al. promoted a flavor network and constructed the food-bridging network [ 65 ]. However, some well-known food combinations such as red wine and beef, do not share chemical compositions or flavor compounds, yet they are still very popular. Therefore, food pairings need to be seen in a broader spctrum, not just based on flavor compounds or chemical composition.

The future of food flavor design could be traced to 2019 when the company McCormick partnered with IBM to use Artificial Intelligence and big data to generate new flavor combinations by analyzing data from millions of datasets to meet the changing consumer demand.

3.5. Take-Out Service

In China, the number of online takeout users accounts for more than 44% of sales, and the scale has exceeded 398 million people ( https://www.qianzhan.com/analyst/detail/220/200512-65621d53.html (accessed on 2 September 2021)). The take-out markets with its large number of users and rapid growth has generated a huge amount of takeout data. The takeout big data service platform not only helps the government supervise the industry, but also creates huge economic and social value. First, it predicts and informs customers of the delivery time, thereby avoiding disrupting consumers’ daily plans and helping restaurants establish a good reputation. Second, it helps the take-out enterprise understand consumer demand. Third, the take-out big data platform promotes the transparency of the supply chain, which is conducive to establishing and improving customers’ trust. Fourth, the overall running of the city can be clearly understood by analyzing the take-out dataset [ 66 ].

Since take-out data involves sensitive private information (the customer’s location, preference, bank, identity, and communication), ensuring data security in the take-oout big data platform is a serious challenge.

3.6. Precise Nutrition and Health Management

The development of big data provides technical support for the processing of massive data, and scientific guidance for human nutrition and health management. In the past, people usually learned nutrition information from experts, books, and the Internet. However, there was a lack of accurate nutrition and health management for individuals because of the difference in individual health conditions [ 67 ]. In an example of applying big data to the people’s daily diet Teng et al. proposed to use a recipe recommendation algorithm to determine which food ingredients were necessary [ 68 ]. Grace et al. combined case-based reasoning and a deep learning algorithm to generate new recipes [ 69 , 70 ]. However, it may also generate "dark cuisine" due to the the uncertain factor of deep learning. Some scholars, like Freyne et al., focused on a diet therapy. They developed a personalized recipe recommendation system for obese people based on the suggestions from medical professionals and research on obese people [ 71 ]. In anoher instance, Yoshida et al. proposed a personalized recipe recommendation based on users’ food preferences [ 72 ]. Zeevi et al. broke with traditional experience-based nutritional recommendations by using machine-learning algorithms to combine data (e.g., blood parameters, dietary habits, and gut microbiota) to formulate personalized diets that optimize postprandial glucose levels and metabolites [ 73 ]. The combination of big data with Artificial Intelligence will provide a new approach for the research of precision nutrition.

4. Challenges

While the food supply chain can benefit from big data, the following challenges need to be addressed.

  • Low data collection efficiency and poor data quality. Due to the lack of effective data collection technology, there are problems with applying big data to food: missing or insufficient data, and difficulties with data forensics. Therefore, it is of great significance to study the collection and verification methods of multisourced big data on food. In the future, edge computing technology will be used to link the food supply chain thereby superseding traditional collection, which is hard to apply to a complex collection environment and collection requirements. Intelligent web crawler technology can be used to collect public opinion data from the Internet, and associate it with data from the physical world to form a complete big data view of food safety. It will solve the dilemma of the separation between physical world and public opinion data found in traditional methods.
  • Data islands. There is one are in the food industry where data is not shared: government. Since regulatory data usually involve state secrets, and enterprise data may involve trade secrets, they reluctance to share these data seriously hinders the application and promotion of big data. Moreover, the independent management systems of enterprises and government creates poor system compatibility, which inhibits data circulation. Therefore, a new business model is urgently needed, for example: the establishment of an incentive mechanism to explore new methods of data fusion to establish an effective privacy protection method and encourage data owners to share data.

Analysis of data security protection technology in the whole life cycle of food data.

Life CycleChallengesProtection Technology
Data collectionData corruption, data loss, data
leakage and data forgery
Data encryption [ ]
Data storageIllegal intrusion and data disclosureStorage encryption [ ], blockchain [ ]
Data transmissionData leakage and data corruptionData encryption [ ], privacy protection [ ], blockchain [ ]
Data usageInformation leakage and data abuseAccess control [ ], SMC [ ], data encryption [ ],
differential privacy protection [ ]
Data DestructionPrivacy disclosure, destruct the data mediaData trusted deletion [ ]
  • Security risks of crowdsourcing services. On one hand, food enterprises have a large amount of enterprise data, but it is difficult to discover the potential value of the information. On the other hand, in crowdsourcing, consumers are not enthusiastic enough to participate, and there is a risk that the crowd will get out of control. In the take-out market, unlicensed crowdsourcing deliverers have become the biggest uncontrollable factor in takeout distribution, and it is a serious risk for take-out food safety. (1) Since the equipment of deliverers is self-regulated, it is difficult to control whether delivery safety standards can be achieved. (2) It is difficult to guarantee the hygiene and health of the people involved in the delivery. If a deliverer carries an infectious disease or the food is contaminated on the way, food safety cannot be guaranteed at all. (3) Take-out deliverers increase the management difficulty of the platform. Recently, the platform can only discipline deliverers through a user account ban. But the deliverer only needs to register with a new mobile phone number and borrow a person’s ID card for real name authentication, and then he can continue to deliver. Therefore, the establishment of a fair, incentive, and perfect crowdsourcing service mechanism will be one of the important research topics.
  • Healthy and sustainable development of the food industry. By 2050, the world’s population will exceed 9 billion, and only through a healthy and sustainable food industry can global food demand be met. [ 89 ]. However, resource waste and foodborne diseases are key factors restricting this sustainable development: excessive chemical residue in crops from the of chemical fertilizers and pesticides [ 90 ]; perishable food loss in developing countries and enormous food waste in developed countries [ 91 ]; high energy consumption and pollution from food processing and transportation; and the discarding of potentially contaminated food because of the iinability to quickly and efficiently trace the source of a contamination. Therefore, the economic and environmental sustainability of stakeholders are the core factors for promoting the sustainable development of the food industry chain.

5. Conclusions

Because big data can provide a large amount of effective business information, the development of data-driven industries has attracted attention from all countries [ 92 ]. In 2012, the United States promoted big data as a national strategy to promote the formation of new economic growth and enhance national competitiveness [ 93 ]. Subsequently, EU member states formulated big data development strategies to transform traditional national governance models [ 94 ]. Big data to promote the development of the food industry has become a main research topic. This paper introduced the application of big data in food industry and showed that the main data sources are regulatory, enterprise, and media data. The results showed the great potential for big data for the food industry. Big data has particularly broad application prospects in social co-governance of the food industry, quantitative production, exploitation of consumption markets, new dishes, take-out services, and precise nutrition and health management. But, to exploit this full potential of big data, technical, social, and health and sustainable development issues require further research.

Author Contributions

Q.T.: Conceptualization, Methodology, Software, Validation and Writing—Original Draft & Review & Editing. H.D.: Investigation. H.W.: Validation and Writing—Review. X.C.: Supervision, and Funding acquisition. All authors have read and agreed to the published version of the manuscript.

The authors would like to acknowledge the support provided by the National Key R&D Program of China (No. 2018YFC1604000).

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Data availability statement, conflicts of interest.

The authors have no competing interest to declare.

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

  • DOI: 10.1007/s10068-024-01543-x
  • Corpus ID: 269789476

A review on bio-based polymer polylactic acid potential on sustainable food packaging.

  • Devi sri Rajendran , Swethaa Venkataraman , +2 authors Vaidyanathan Vinoth Kumar
  • Published in Food Science and… 1 June 2024
  • Materials Science, Environmental Science, Agricultural and Food Sciences

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