Perspective Cite This: J. Agric. Food Chem. 2018, 66, 7562−7567
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Recent Developments and Digital Perspectives in Food Safety and Authenticity Jan Fritsche*
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Department of Safety and Quality of Milk and Fish Products, Federal Research Institute of Nutrition and Food, Max Rubner-Institut, Hermann-Weigmann-Straße 1, 24103 Kiel, Germany ABSTRACT: Food safety is of fundamental importance for the food processing industry, food retailers and distributors, and competent authorities because of its potentially direct impact on the health of consumers. Next to the prevention of microbiological, chemical, and physical hazards, increasing efforts are currently made to combat risks associated with food fraud or food authenticity. Food safety management systems nowadays comprise food safety, food defense, and food fraud prevention measures, trying to cope with the increasing complexity and globalization of the food supply chains. Future digital opportunities include the prediction of food safety and food authenticity issues by handling structured and unstructured data retrieved from various sources and origins to ensure the health of consumers and to minimize economical losses. KEYWORDS: food authenticity, food fraud, food safety, RASFF, EMA database
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INTRODUCTION Food Safety. The global population is expected to reach at least 9 billion by the year 2050, requiring up to 70% more food and demanding food production systems and the food chain to become fully sustainable. This great challenge is interfered by a number of overarching issues, including increasing complexity of food supply chains, environmental stress, a growing aging population, and changing patterns of consumer choice and food consumption. Within this context, food safety must become an enabler of global food security and refers to food defense and food fraud issues, including food authenticity. The following perspective focuses on recent developments of the latter. Food safety management systems thus far have generally focused on the unintentional contamination of food by known ingredients, pathogens, mishandling, or processing. While the vast majority of reported food fraud cases did not result in a threat to consumer health, some acts of food fraud, such as adding adulterant substances, can become dangerous. Unfortunately, criminals usually lack the motivation and/or knowledge to determine whether or not their actions will result in hygienic or chemical risks to consumers. Therefore, food fraud activities have the potential to cause food safety issues. Food Authenticity. Food authenticity, food adulteration, food crime, and food fraud are frequent terms used in the public domain. Food fraud encompasses a wide range of deliberate fraudulent acts, usually with intentional and economic motivation, including addition, dilution, tampering, or misrepresentation of food, food ingredients, or food packaging, and includes false or misleading statements made about a product. These clusters are similar to an analysis of the fraud notifications in the European Union (EU) Rapid Alert System for Food and Feed (RASFF), where false, improper, or missing labels/documents were the most common type of food fraud and products of animal origin and dietary supplements were the most frequently adulterated products.1 However, © 2018 American Chemical Society
missing labels/documents are not automatically associated with food fraud. Food supply chain fraud can embrace the integrity of the food item, the manufacturing processes used to produce that food item, and/or the people employed and the data accompanying the food item. Relevant key players against food fraud actions, for example, are the EU Food Fraud Network, the U.K. Food Standards Agency (FSA-UK), the Global Food Safety Initiative (GFSI), the Safe Supply of Affordable Food Everywhere (SSAFE), the U.S. Pharmacopeia (USP), the U.S. Food and Drug Administration (FDA), and the Chinese National Center for Food Safety Risk Assessment (CFSA). The Grocery Manufacturers Association in the U.S.A. estimated that global food fraud costs between $10 and $15 billion per year, affecting approximately 10% of all commercially sold food products.2 A single incident can permanently destroy a valuable brand, cause long-term industry-wide losses, close off export markets, and damage trust in public institutions. According to Elliott,3 the increase of the complexity of supply chain networks, the rapid development of technology (internet, printing, mobile phones, etc.), and the rapid growth of warehouse systems and refrigerated transport are the main drivers for an increase in food fraud opportunities. As a result of the impact on economies and consumer confidence, governments are holding competent food authorities accountable for protecting against all food risks and vulnerabilities, including food safety and food authenticity or food fraud (EU Regulation 2017/625). Regulatory Activities and Compliance. The EU has defined the official food and feed controls for Member States (MS) in Regulation (EC) 882/2004, which ensures the effective implementation of Regulation (EC) 178/2002. Received: Revised: Accepted: Published: 7562
February 14, 2018 June 19, 2018 June 19, 2018 June 19, 2018 DOI: 10.1021/acs.jafc.8b00843 J. Agric. Food Chem. 2018, 66, 7562−7567
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missing documents. From 2010 to 2013, a total of 749 notifications were reported in the RASFF database under the hazard category “adulteration/fraud”, which comprises six different fraud classes: (1) improper, fraudulent, missing, or absent health certificates, (2) illegal importations, (3) tampering, (4) improper, expired, fraudulent, or missing common entry documents (CED), (5) expiration date, and (6) mislabeling. Fraud notifications in RASFF have been analyzed in a few studies. The work from Kleter et al.4 covers the period 2003− 2007. Within this period, a total of 248 notifications of fraudrelated issues were reported in RASFF, with the majority related to illegal imports and lack of authorization of establishment and transits. The predominant products were meat (23%) and seafood (19%), followed by composite and mixed products (17%). Most frauds originated from Asia (44%) and the EU (22%). The number of notifications increased after mid-2005.4 Comparable data were provided by Tähkäpää et al.1 for the following period (2008−2012). Fish and fish products and meat and meat products were the most frequently reported product categories, with 16 and 11%, respectively. Asia was again by far the most common region of origin (45%). Although food fraud per se does not result in a high number of notifications (2%),1 it has the potential to become a real health risk to humans and animals as a result of unpredictable risk management situations. Therefore, control and food fraud mitigation strategies need to be developed and applied, including increased border efficiency to allow for a fast detection of food fraud and management actions. In the U.S.A., the U.S. Food Safety and Modernization Act (FSMA) was passed in January 2011, and in 2015, the FSMA Preventative Controls rule (FSMA-PC) was published, which included directions with respect to food fraud and “economically motivated adulteration” (EMA) in the final rule. It is noteworthy that the RASFF database has a completely different fraud-type classification approach than the EMA and USP databases. EMA classifies nine types of food fraud, for example, substitution, dilution, artificial enhancement, counterfeit, transshipment, and misbranding, whereas, for the USP database, only three distinguished food fraud classes are applied: addition, replacement, and removal. Food Fraud Assessment. Conventional and emerging analytical techniques used to identify food fraud involve sensory, physicochemical, DNA-based, chromatographic, and spectroscopic methods. In addition, these methods have been successfully combined with chemometrics, making these techniques more convenient and effective for the analysis of a broad variety of food products. Despite recent advances in the development of analytical methods, the demand for novel suitably sensitive and widely applicable and validated methodologies that encompass all of the various aspects of food adulteration still exists. Analytical Detection and Verification. For the analysis of food authenticity or the identification/verification of food fraud, comprehensive analytical methods are commonly applied. For that purpose, targeted and non-targeted approaches have been developed on the basis of various analytical methods. For example, Fourier transform infrared spectroscopy (FTIR) has already been successfully implemented in food fraud detection.5 DNA sequencing techniques are also considered to be reliable; nevertheless, their limitations have been discussed6 and make them unsuitable to be employed as standalone tools in the field of authentication.
According to Regulation (EC) 882/2004, each MS has to design an annual plan for their official controls within its territory. These plans should be planned and carried out with a “risk-based” approach at all stages of production, processing, and distribution. Controls at the “point of entry” from third countries into the EU are included in Regulation (EC) 882/ 2004, with additional controls in Regulation (EC) 669/2009 including the frequency of official controls for particular food (or feed) items from specific countries of origin. The Official Controls Regulation (OCR, Regulation EU 625/2017) replaces the current regulation on official controls [Regulation (EC) No 882/2004]. It provides the framework for MS to verify that businesses comply with agri-food chain rules, encompassing all activities from farm to fork, including import controls and export certificates. Agri-food chain rules cover holistically the safety and quality of food and feed (including fraud) as well as plant health and animal health and welfare. Digital Information Management Systems for Official Controls. At the European level, the European Commission (EC) may, by means of implementing acts, designate EU reference centers that will support the activities of the EC and MS to prevent, detect, and combat violations of the rules referred to in article 1(2) perpetrated through fraudulent or deceptive practices (article 97, EU Regulation 625/2017). In the light of the revision of Regulation (EC) 882/2004, the German Federal Ministry of Food and Agriculture (BMEL) in 2016 introduced a dedicated National Reference Center for the Authenticity and Integrity of the Food Chain under the leadership of the Federal Research Institute of Nutrition and Food, Max Rubner-Institute (MRI; for more information about the MRI, visit MRI). The tasks of that national reference center will comprise the provision of (digital) information on food fraud for the competent authorities in Germany, the development of accredited analytical methods for the detection of food fraud, and the development of certified reference material and authentic food samples. IMSOC is the Information Management System for Official Control, a new interconnected information technology (IT) system that needs to be implemented according the new Regulation 2017/625 on official controls and will include among others TRACES, the multilingual online management tool of EC for all sanitary and organic requirements on intraEU trade and importation of animals, semen and embryos, food, feed, and plants; Europhyt, a notification and rapid alert system dealing with interceptions for plant health reasons of consignments of plants and plant products imported into the EU or being traded within the EU itself; and RASFF, a key tool to ensure the flow of information, which enables a swift reaction, when risks to public health are detected in the food chain. In the EU, RASFF has been established as part of Regulation (EC) 178/2002 to support the control and safety of food and animal feed on the European market. Under RASFF, members [EU-28 national competent authorities, EC, European Food Safety Agency (EFSA), European Space Agency (ESA), Iceland, Norway, Lichtenstein, and Switzerland] exchange information electronically, e.g., news, notifications, and alerts, which are available through the RASFF portal. On this internet portal, interactive searches can be performed in the RASFF database. The RASFF database includes both intentional food frauds, for example, fraudulent documents or adulteration cases, and unintentional frauds, such as improper, expired, or 7563
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The data acquisition module saves all matching data from webpages for later analysis and documentation of evidence. The image analysis tool performs logo recognition to supplement the text-based information on websites. The monitoring tool performs regular automated monitoring of online vendors, products, and ingredients. The principle of the prototype was proven by conducting a web search for potentially hazardous food products containing synephrine and caffeine. In total, 1242 offerings of suspicious food products were identified on the internet among the 8683 qualified search results. Thus, this prototype has shown the potential to strengthen consumer protection with respect to online marketed foods.12 Food Fraud Prevention: Risk Assessment and Risk Management. Next to the competent authorities around the world, food fraud prevention is also a hot topic for the agrifood business. For that reason, quality management systems are currently adapted to include food fraud vulnerability assessment tools. As is usual for quality management systems, food fraud management systems are based on a continuous process typically following an enterprise risk management (ERM) and plan−do−check−act (PDCA) approach (Figure 2). It typically starts with an evaluation to characterize the food
Furthermore, according to European legislation concerning the performance of analytical methods,7 those approaches would be classified as screening methods, and as such, they are not fit for the purpose of confirming adulteration to required legal standards. This is due to a lack of chemical structure confirmation, if the legal action is to be pursued by industry or regulators. High-resolution mass spectrometry (HRMS)-, isotope-ratio mass spectrometry (IRMS)-, and nuclear magnetic resonance (NMR) spectroscopy-based fingerprinting approaches have also been employed in food authentication. However, even though such methods provide the possibility of detecting unusual deviations within the sample set, instrumental analysis is time-consuming and requires expensive equipment, data storage facilities, and high processing power.8 Available targeted mass-spectrometry-based methods designed for the purpose of confirming adulteration usually employ food-profiling approaches, whereby sample classification is based on analyzing a selected group of matrix constituents, such as flavonoids in Ginkgo biloba or ginsenosides in Panax ginseng.9,10 Such validated analytical methods ensure the accuracy of analytical determination;8 however, they are prone to “targeted designed adulteration”11 as a result of increased knowledge regarding chemical composition of food commodities. Overall, designing an analytical approach suitable for rapid, low-cost, and reliable detection and confirmation of adulteration still presents a great challenge with respect to the experimental design [e.g., selectivity, specificity, chosen (bio)markers, etc.]. Digital Approaches To Combat Food Fraud. A novel concept for the automated computer-aided identification and evaluation of potentially non-compliant food products traded via electronic commerce was developed by Krewinkel et al.12 That prototype (AAPVL) was developed applying a modular architecture consisting of a research tool, an image analysis tool, and a monitoring tool (Figure 1). The research tool uses both internet search engines and customized search algorithms.
Figure 2. Food fraud vulnerability assessment following an ERM and PDCA approach (unpublished data from Fritsche).
fraud vulnerabilities, which includes knowledge about used materials and existing risks (e.g., geographical origins, history, economic factors, incident review, and emerging issues), active suppliers, supply chain (length, complexity, and supply and demand arrangements), and existing control measures. The next step is the design and review of mitigation strategies and their implementation. As soon as changes in the supply chain for an ingredient or a newly identified adulterant for an ingredient are known, which may impact the previously identified vulnerabilities, the entire process must be reassessed to ensure its continued effectiveness. For more details on the food fraud initial screening (FFIS), visit Spink et al.13 and Committee of Sponsoring Organizations of the Treadway Commission (COSO) literature.14 It is notable that GFSI
Figure 1. AAPVL concept for an automated computer-aided identification and evaluation of potentially non-compliant food products traded via e-commerce (unpublished data from Krewinkel). 7564
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Figure 3. Multisource/multidomain approach for the prevention of food fraud (unpublished data from Fritsche).
food production chain that is able to combine underlying databases, interactions, and feedback loops of the drivers, as required for a holistic approach. One promising approach, however, is using a Bayesian network (BN). Novel food fraud prediction approaches should consist of multisource/multidomain strategies, combining relevant information from the food business information domain, the competent authorities information domain, and the public domain (Figure 3). That kind of big data approach will most likely be able to provide the largest data pool for food fraud prediction models. Data access rights/data ownership as well as streamlining data plausibility will be the biggest legal challenge for a successful food safety risk management. Ultimately, global information contracts between all stakeholders involved, agri-food business, competent authorities, and the public domain alike, are foreseen as a long-term requirement for food fraud predictions. BN. BNs are a class of probabilistic models originating from Bayesian statistics and decision theory in addition to graph theory.22,23 The structure of a BN contains nodes (i.e., random variables) connected by directed arcs, reflecting a dependence structure between the nodes. BNs are applied in many fields, such as hazard and risk assessment,24,25 fraud detection,26,27 accident prevention,28 and food fraud prediction.29,30 BNs have the ability to integrate different data sources and types, such as expert knowledge, measurement data, and feedback experience31,32 using Bayes’ theorem. Case Study: Herbs and Spices. Recently, Bouzembrak et al.33 developed a BN model to predict the presence of food safety hazards in herbs and spices to guide risk-based monitoring (prediction accuracy of 85%). This suggested approach consisted of a three-step process: (1) data collection on the main factors (hazards, product, and country of origin) using different data sources, such as KAP (Dutch national monitoring program for chemical contaminations in food and feed) and RASFF databases, (2) construct a BN model including nodes, arrows, states, and parameters for each node in the form of conditional probability tables (CPTs), and (3) validate the model using an independent data set.
compliance already requires a food fraud vulnerability assessment and mitigation plan, which forces food companies into action. More recently, SSAFE, in cooperation with PricewaterhouseCoopers (PwC), Wageningen University, and Vrije Universiteit Amsterdam and in consultation with food industry leaders around the world, developed a free-of-charge food fraud vulnerability assessment app, which supports the GFSI requirements (for more details, visit www.pwc.com/ foodfraud). Prediction of Food Safety Incidents and Food Fraud. Various researchers and risk managers have explored possibilities to forecast food safety and food fraud incidents by monitoring trends and events. International as well as local developments may directly or indirectly influence the performance of food-producing systems, for example, climate change, economy, and trade shifts, but also human behavior (e.g., bioterrorism or sabotage), and new technologies.15−18 The 10 key drivers recently identified in a scoping study on food safety and nutrition19 were (1) global economy and trade, (2) global cooperation and standard setting, (3) governance, (4) demography and social cohesion, (5) consumer attitudes and behavior, (6) new food chain technologies, (7) competition for key resources, (8) climate change, (9) emerging food chain risks and disasters, and (10) new agri-food chain structures. The Food and Agriculture Organization of the United Nations (FAO)20 has adopted a holistic approach that includes a full host environment analysis of the entire food chain in which the driving forces of food safety risks are identified and assessed, including associated data sources from various origins. A similar approach has also been considered for climate-induced food safety risks.16,18,21 Application of the holistic approach to identify known and emerging food safety risks needs a model that links all drivers and their dependencies. It also needs to connect underlying databases from various sources and origins. A suitable model should access data on the drivers, process these data, and perform calculations to provide predictions on emerging food safety and food fraud risks, preferably in real time. Up to now, no modeling approach or system has been developed for the 7565
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Journal of Agricultural and Food Chemistry A summary of the most common hazards, products, and countries of origin reported for herbs and spices is shown in Table 1.
affected producta
countries of origin
mycotoxins (1422) pesticides (528) pathogens (459) composition (399)
paprika (398) chili pepper (330) curry leaves (279)
RASFF (2005−2014) India (32%) Thailand (12%) Vietnam (5%) KAP (2005−2011) India (23%) Netherlands (11%) Indonesia (8%)
ACKNOWLEDGMENTS
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REFERENCES
(1) Tähkäpäa,̈ S.; Maijala, R.; Korkeala, H.; Nevas, M. Patterns of food frauds and adulterations reported in the EU rapid alert system for food and feed and in Finland. Food Control 2015, 47, 175−184. (2) Manning, L.; Soon, J. M. Food Safety, Food Fraud, and Food Defense: A Fast Evolving Literature. J. Food Sci. 2016, 81, R823− R834. (3) Elliott, C. Elliott Review into the Integrity and Assurance of Food Supply NetworksFinal Report: A National Food Crime Prevention Framework; Her Majesty’s (HM) Government: London, U.K., 2014; https://www.gov.uk/government/publications/elliott-review-intothe-integrity-and-assurance-of-food-supply-networks-final-report (accessed Jan 5, 2018). (4) Kleter, G. A.; Prandini, A.; Filippi, L.; Marvin, H. J. Identification of potentially emerging food safety issues by analysis of reports published by the European Community’s Rapid Alert System for Food and Feed (RASFF) during a four-year period. Food Chem. Toxicol. 2009, 47, 932−950. (5) Ellis, D. I.; Muhamadali, H.; Haughey, S. A.; Elliott, C. T.; Goodacre, R. Point-and-shoot: Rapid quantitative detection methods for on-site food fraud analysis − moving out of the laboratory and into the food supply chain. Anal. Methods 2015, 7, 9401−9414. (6) Parveen, I.; Gafner, S.; Techen, N.; Murch, S. J.; Khan, I. A. DNA Barcoding for the Identification of Botanicals in Herbal Medicine and Dietary Supplements: Strengths and Limitations. Planta Med. 2016, 82, 1225−1235. (7) European Commission (EC).. 2002/657/EC: Commission Decision of 12 August 2002 implementing Council Directive 96/ 23/EC concerning the performance of analytical methods and the interpretation of results. Off. J. Eur. Communities: Legis. 2002, 45, 8− 36. (8) Esslinger, S.; Riedl, J.; Fauhl-Hassek, C. Potential and limitations of non-targeted fingerprinting for authentication of food in official control. Food Res. Int. 2014, 60, 189−204. (9) Xie, B. D.; Wang, H. T. Effects of light spectrum and photoperiod on contents of flavonoid and terpene in leaves of Ginkgo biloba L. J. Nanjing For. Univ. 2006, 30, 51−54. (10) Yuan, J.; Chen, Y.; Liang, J.; Wang, C. Z.; Liu, X.; Yan, Z.; Tang, Y.; Li, J.; Yuan, C. S. Component analysis and target cell-based neuroactivity screening of Panax ginseng by ultra-performance liquid chromatography coupled with quadrupole-time-of-flight mass spectrometry. J. Chromatogr. B: Anal. Technol. Biomed. Life Sci. 2016, 1038, 1−11. (11) Sanzini, E.; Badea, M.; Dos Santos, A.; Restani, P.; Sievers, H. Quality control of plant food supplements. Food Funct. 2011, 2, 740− 746. (12) Krewinkel, A.; Sünkler, S.; Lewandowski, D.; Finck, N.; Tolg, B.; Kroh, L. W.; Schreiber, G. A.; Fritsche, J. Concept for automated computer-aided identification and evaluation of potentially noncompliant food products traded via electronic commerce. Food Control 2016, 61, 204−212. (13) Spink, J.; Moyer, D. C.; Speier-Pero, C. Introducing the Food Fraud Initial Screening model (FFIS). Food Control 2016, 69, 306− 314. (14) Committee of Sponsoring Organizations of the Treadway Commission (COSO). Welcome to COSO; https://www.coso.org (accessed Jan 6, 2018). (15) Boland, M. J.; Rae, A. N.; Vereijken, J. M.; Meuwissen, M. P. M.; Fischer, A. R. H.; van Boekel, M. A. J. S.; Rutherfurd, S. M.; Gruppen, H.; Moughan, P. J.; Hendriks, W. H. The future supply of animal-derived protein for human consumption. Trends Food Sci. Technol. 2013, 29, 62−73.
a
The numbers in parentheses indicate the number of incidents.
The BN model could help to target sampling and analyses of products based on hazard prediction, e.g., decide if investigations should be focused at the market level or border inspection.33
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CONCLUSION AND EXPECTATIONS Over the past decade, the focus of food safety developments around the world shifted from the traditional food safety hazard analysis critical control point (HACCP) principle to food defense (after 9/11 in the U.S.A.) and, most recently, to food fraud prevention. The latter is heavily pushed forward in the global agenda of food safety as a result of substantial regulatory activities in the U.S.A., China, and Europe but also the engagement of the global agri-food business to prevent food fraud. Joint efforts making use of the food business information domains and the information domains of competent authorities at the global level could contribute to minimize economical losses and improve the confidence of consumers in food safety and quality (Figure 3). Closer international cooperations between national authorities at the strategic and operational level as well as improved and enlarged IT-based data exchange systems along the entire agri-food chain would contribute to that goal. Cooperations are considered prerequisites to evolve food safety in the 21st century. Digital challenges will be the development of novel and reliable food fraud predication approaches, applying, e.g., artificial neural networks and/or probabilistic models, such as a BN, and enabling adequate and timely resource allocations within the food control community as well as the agri-food business. Ultimately, big data solutions will also require significant changes in the education and qualification of future employees of competent authorities and the agri-food business alike.
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The author thanks Prof. Martin Holle (Hamburg University of Applied Sciences, Germany) for fruitful discussions on legal aspects.
Table 1. Most Common Hazards Reported for Herbs and Spices in the KAP and RASFF Databases hazard categorya
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AUTHOR INFORMATION
Corresponding Author
*Telephone: +49-431-609-2250. Fax: +49-431-609-2300. Email:
[email protected]. ORCID
Jan Fritsche: 0000-0002-9620-8898 Notes
The author declares no competing financial interest. 7566
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