Recent Developments and Digital Perspectives in Food Safety and

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Recent Developments and Digital Perspectives in Food Safety and Authenticity Jan Fritsche J. Agric. Food Chem., Just Accepted Manuscript • DOI: 10.1021/acs.jafc.8b00843 • Publication Date (Web): 19 Jun 2018 Downloaded from http://pubs.acs.org on June 25, 2018

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Journal of Agricultural and Food Chemistry

The AAPVL-concept for an automated computer-aided identification and evaluation of potentially noncompliant food products traded via e-commerce (A. Krewinkel unpublished data). 152x149mm (150 x 150 DPI)

ACS Paragon Plus Environment


Food Fraud Vulnerability Assessment following an Enterprise Risk Management (ERM) and PDCA approach (J. Fritsche unpublished data). 254x190mm (96 x 96 DPI)


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Multi-source/multi-domain approach for the prevention of food fraud (J. Fritsche unpublished data). 275x190mm (96 x 96 DPI)



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Recent Developments and Digital Perspectives in Food Safety and Authenticity

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Jan Fritsche*

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ABSTRACT (max. 100 words)

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Food Safety is of fundamental importance for the food processing industry, the food retailers and

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distributors, as well as for the competent authorities because of its potentially direct impact on

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consumers’ health. Next to the prevention of microbiological, chemical, and physical hazards

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increasing efforts are currently made to combat risks associated with food fraud or food authenticity.

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Food Safety Management Systems nowadays comprise food safety, food defense, and food fraud

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prevention measures trying to cope with the increasing complexity and globalization of the food

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supply chains. Future digital opportunities include the prediction of food safety and food authenticity

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issues by handling structured and unstructured data retrieved from various sources and origins in

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order to ensure consumers’ health and to minimize economical losses.

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KEYWORDS

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Food authenticity, food fraud, food safety, food supply chain, prediction models, RASFF, big data,

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EMA database, National Reference Center for the Authenticity and Integrity of the Food Chain, e-

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foods, HACCP, IMSOC, industry 4.0, TRACES, Europhyt, Global Food Safety Initiative

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Corresponding author:

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Max Rubner-Institut, Federal Research Institute of Nutrition and Food, Department of Safety and Quality of Milk and Fish Products, Hermann-Weigmann-Str. 1, 24103 Kiel, Germany

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Phone: +49-431-609-2250 Fax: +49-431-609-2300 E-mail: [email protected]

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Introduction

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Food Safety

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There is an urgent need to drive improvements in the efficiency and effectiveness of food chains. The

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global population is expected to reach at least 9 billion by the year 2050, requiring up to 70% more

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food, and demanding food production systems and the food chain to become fully sustainable. This

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great challenge is complicated interfered by a number of overarching issues, including increasing

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complexity of food supply chains, environmental constraintsstress, a growing aging population, and

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changing patterns of consumer choice and food consumption. Within this context, food safety must

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become an enabler of global food security and refers to food defense and food fraud issues including

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food authenticity. The following perspective article focuses on recent developments of the latter.

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Food safety management systems so far have generally focused on the unintentional contamination

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of food by known ingredients, pathogens, mishandling, or processing. While the vast majority of

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reported food fraud cases did not result in a threat to consumer health, some acts of food fraud,

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such as adding adulterant substances, can become dangerous. Unfortunately, criminals usually lack

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the motivation and/or expertise knowledge to determine whether or not their actions will result in

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hygienic or chemical risks to consumers. Therefore, food fraud activities have the potential to cause

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food safety issues.

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Food Authenticity

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Food authenticity, food adulteration, food crime, and food fraud are frequent terms used in public

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domain. Food fraud encompasses a wide range of deliberate fraudulent acts, usually with intentional

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and economic motivation including addition, dilution, tampering, or misrepresentation of food, food

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ingredients, or food packaging; and includes false or misleading statements made about a product.

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These clusters are similar to an analysis of the fraud notifications in the European Union Rapid Alert 2



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System for Food and Feed (RASFF) where false, improper, or missing labels/documents were the

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most common type of food fraud and products of animal origin and dietary supplements were the

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most frequently adulterated products [1]. However, missing labels/documents are not automatically

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associated with food fraud. Food supply chain fraud can embrace the integrity of the food item, the

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manufacturing processes used to produce that food item, and/or the people employed and the data

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that accompanyingies the food item.

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Relevant key players against food fraud actions are for example are the EU Food Fraud Network, the

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UK Food Standards Agency (FSA-UK), the Global Food Safety Initiative (GFSI), the Safe Supply of

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Affordable Food Everywhere (SSAFE), the US Pharmacopeia (USP), the US Food and Drug

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Administration (FDA), and the Chinese National Center for Food Safety Risk Assessment (CFSA). The

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Grocery Manufacturers Association in the USA estimated that global food fraud costs between $10

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billion and $15 billion per year, affecting approximately 10% of all commercially sold food products

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[2

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losses, close off export markets, and damage trust in public institutions. According to Elliott [3]3,

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increase of the complexity of supply chain networks, the rapid development of technology (internet,

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printing, mobile phone etc.), and the rapid growth of warehouse systems and refrigerated transport

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are the main drivers for an increase in food fraud opportunities.

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Due to the impact on economies and consumer confidence, governments are holding competent

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food authorities accountable for protecting against all food risks and vulnerabilities including food

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safety and food authenticity or food fraud (regulation Regulation EU 2017/625).

.].

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A single incident can permanently destroy a valuable brand, cause long-term industry-wide Formatted: Superscript

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Regulatory Aactivities and cCompliance

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In tThe European Union (EU),has defined the official food and feed controls for Member States (MS)

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are defined in Regulation (EC) 882/2004, which ensures the effective implementation of Regulation

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(EC) 178/2002. According to Regulation (EC) 882/2004, each Member State has to designs an annual

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plan for their official controls within its territory. These plans should be planned and carried out with 3


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a “risk-based” approach at all stages of production, processing, and distribution. Controls at the

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“point of entry” from third countries into the EU are included in Regulation (EC) 882/2004,; however,

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additional controls are defined in Regulation (EC) 669/2009 including. According to Regulation (EC)

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669/2009, the frequency of official controls for of a particular food (or feed) items from a specific

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countriesy of origin is defined and is enforced in every each Member State.

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The Official Controls Regulation (OCR; Regulation EU 625/2017) replaces the current regulation on

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official controls (Regulation (EC) No 882/2004). It provides the framework for Member States (MS) to

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verify that businesses comply with agri-food chain rules, encompassing all activities from farm to fork

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including import controls and export certificates. The OCR replaces the current regulation on official

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controls (Regulation (EC) No 882/2004 ). The agri-food chain encompasses all those activities that

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range from farm to fork. AAgri-food chain rules therefore cover holistically the safety and quality of

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food and feed (including fraud), as well as plant health, animal health and welfareincluding fraud.,

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differentiate quality of food and feed, plant health, animal health and welfare. They also cover

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import controls on goods entering the EU from third countries and export certificates.

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Digitial iInformation mManagement Ssystems for Official Controls

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At the European level, the European Commission may, by means of implementing acts, designate

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European Union reference centrers that shall support the activities of the Commission and of the

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Member States to prevent, detect, and combat violations of the rules referred to in Article 1(2)

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perpetrated through fraudulent or deceptive practices (article 97 regulation Regulation EU

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625/2017). In the light of the revision of the Regulation (EC) 882/2004, the German Federal Ministry

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of Food and Agriculture (BMEL) in 2016 introduced in 2016 a dedicated National Reference Center

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for the Authenticity and Integrity of the Food Chain under the leadership of the Max Rubner-

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Institute, Federal Research Institute of Nutrition and Food (for more information about the MRI visit

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MRI). The tasks of that National Reference Center will comprise the provision of (digital) information 4



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on food fraud for the competent authorities in Germany, the development of accredited analytical

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methods for the detection of food fraud, and the development of certified reference material and

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authentic food samples.

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IMSOC is the Information Management System for Official Control, so a new interconnected IT

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system that needs to be implemented according the new Regulation 2017/625 about on official

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controls and will include among others:

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TRACES: European Commission's multilingual online management tool for all

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sanitary and organic requirements on intra-EU trade and importation of animals,

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semen and embryos, food, feed, and plants.

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Europhyt: a notification and rapid alert system dealing with

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Interceptionsinterceptions for plant health reasons of consignments of plants and

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plant products imported into the EU or being traded within the EU itself.

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RASFF: a key tool to ensure the flow of information, which enables a to enabling swift reaction, when risks to public health are detected in the food chain is RASFF.

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In the EU, the Rapid Alert System for Food and Feed (RASFF) has been established as part of the

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Regulation (EC) 178/2002 in order to support the control and safety of food and animal feed on the

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European market. Under RASFF, members (EU-28 national competent authorities, the European

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Commission, the European Food Safety Agency (EFSA), European Space Agency (ESA), Iceland,

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Norway, Lichtenstein, and Switzerland) exchange information electronically, e.g. news, notifications,

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and alerts, which are available through the RASFF portal. On this Iinternet portal interactive searches

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can be performed in the RASFF database. The RASFF database includes both intentional food frauds,

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for example fraudulent documents or adulteration cases, as well as unintentionally frauds, such as

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improper, expired, or missing documents. From 2010 until to 2013 in a total of 749 notifications were

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reported in the RASFF database under the hazard category “adulteration/fraud”, which comprises of 5


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6 different fraud classes: (i1) improper, fraudulent, missing, or absent health certificates, (ii2) illegal

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importations, (iii3) tampering, (iv4) improper, expired, fraudulent, or missing common entry

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documents (CED), (v5) expiration date, and (6vi) mislabeling.

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Fraud notifications in RASFF have been analyszed in a few studies., The work from such as by Kleter

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et al. 4 and co-workers coversfor the period 2003-2007 [4] and by Tähkäpää and co-workers for the


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period 2008-2012 [1]. Within this periodIn the period From 2003 -to 2007, ina total of 248


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notifications of fraud related issues were reported in RASFF, the majority pertained related to illegal

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imports and lack of authorization of establishment and of transits. The predominante products were

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meat (23%), seafood (19%), and followed by composite and mixed products (17%). Most frauds and

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the products originated mainly from Asia (44% of the notifications) and EU (22% of the notifications).

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An increase in tThe number of reports notifications increased was noticed after mid-2005 [4].


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Comparable data were provided Similar results were observed by Tähkäpää et al.and co-workers1 [1]

Comment [NH1]: s.o.

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for in during the following period (2008-2012). Fish and fish products and meat and meat products

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were the most frequently reported product categories, with 16% and 11%, respectively. and Asia was

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again by far (45%) the most common region of origin (45%). Although food fraud per se does not

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result in a high number of notifications (2%) [1], it has the potential to become a real health risks tofor

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humans and animals due to unpredictable risk management situations. and t Therefore, control and

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food fraud mitigation strategies should need to be developed and applied including increased border

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to increase the border efficiency of border in order and to allow a fast detection of food fraud and

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management actions.

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In the USA, the US Food Safety and Modernization Act (FSMA) was passed in January 2011 and in

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2015 the FSMA Preventative Controls rule (FSMA-PC) was published, which included directions with

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respect to Food Fraud and “Economically Motivated Adulteration” (EMA) in the final rule (FDA,

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2009). It is notable thatnoteworthy that the RASFF database has a completely different fraud type

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classification approach than compared to the EMA and USP databases., EMA which classifiesd 9 types

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of food fraud, for example, substitution, dilution, artificial enhancement, dilution, counterfeit,


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transshipment, counterfeit, and misbranding, whereas . In the USP database only 3 distinguishedd

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food fraud classes are applied: addition, replacement, and removal.

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Food Fraud Assessment

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Conventional and emerging The analytical techniques (both conventional and emerging) used to

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identify food fraud involve sensory, physicochemical, DNA-based, chromatographic, and

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spectroscopic methods. ,In addition, these methods and have been successfully combined with

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chemometrics, making these techniques more convenient and effective for the analysis of a broad

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variety of food products. Despite recent advances in the development of analytical methods, the

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demand need remains for novel suitably sensitive and widely applicable and validated methodologies

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that encompass all the various aspects of food adulteration is still existing.

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Analytical Detection & Verification

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For the analysis of food authenticity or the identification/verification of food fraud comprehensive

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analytical methods are commonly applied. For that purpose targeted and non-targeted approaches

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have been developed based on various analytical methods. , fFor example, Fourier-Transform

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Infrared spectroscopy (FTIR) has already been already successfully implemented in food fraud

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detection [5]. DNA sequencing techniques are also considered to be reliable, nevertheless, their


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limitations have been discussed [6] and make them unsuitable to be employed as stand-alone tools in


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the field of authentication. Furthermore, according to European legislation concerning the

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performance of analytical methods [7], those approaches would be classified as screening methods

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and as such they are not fit for the purpose of confirming adulteration to required legal standards.

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This is due, due to a lack of chemical structure confirmation, if the legal action is to be pursued by

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industry or regulators. High Resolution Mass Spectrometry (HRMS), Isotope-ratio mass spectrometry


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(IRMS), and Nuclear magnetic resonance (NMR) spectroscopy based fingerprinting approaches have

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also been employed in food authentication., hHowever, even though such methods provide the

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possibility of detecting unusual deviations within the sample set, instrumental analysis is time

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consuming, requires expensive equipment, data storage facilities, and high processing power [8].

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Available targeted mass spectrometry based methods designed for the purpose of confirming

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adulteration confirmation usually employ food profiling approaches, whereby sample classification is

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based on analyszing a selected group of matrix constituents, such as flavonoids in Ginkgo biloba or

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ginsenosides in Panax Ginseng [9,; 10]. Such validated analytical methods ensure the accuracy of the


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analytical determination [8], however, they are prone to ‘targeted designed adulteration’ [11] due to


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increased knowledge regarding chemical composition of food commodities. Overall, designing an

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analytical approach suitable for rapid, low-costcheap, and reliable detection and confirmation of

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adulteration still presents a great challenge with respect to the experimental design (e.g. selectivity,

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specificity, chosen (bio)markers, etc.).



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Digital aApproaches to cCombat Food Fraud

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A novel concept for the automated computer-aided identification and evaluation of potentially non-

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compliant food products traded via electronic commerce was developed by Krewinkel et al. [12]. That

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AAPVL prototype (AAPVL) was developed using applying a modular architecture consisting of

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comprising a research tool, an image analysis tool, and a monitoring tool (see Figure 1). The research

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tool utilizes both iInternet search engines and customized search algorithms. The data acquisition

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module stores saves all matching data from webpages for later analysis and preservation

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documentation of evidence. The image analysis tool performs logo recognition in order to

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supplement enrich the text-based information of websites. The monitoring tool performs regular

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automated monitoring of e-foodonline vendors, products, and ingredients. The proof of principle of

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the prototype was achieved proven by conducting a web search for potentially hazardous food

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products containing synephrine and caffeine. In total, 1242 product offerings on the internet for of 8




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suspicious food products were identified on the Internet among the 8683 qualified search results.

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Thus, that this prototype has shown the potential to enhance strength consumer protection and food

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safety with respect to online marketed e-foods [12].


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Food Fraud Prevention: Risk Aassessment and rRisk mManagement

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Next to the competent authorities around the world, food fraud prevention is also a hot topic for the

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agri-food business. For that reason, quality management systems are currently adapted to include

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food fraud vulnerability assessment tools. As is usual for quality management systems, food fraud

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management systems are based on a continuous process typically following an Enterprise Risk

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Management (ERM) and PDCA approach (see Figure 2). It Ttypically it starts with an evaluation to

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characterize the food fraud vulnerabilities, which includes knowledge about used materials and

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existing risks (e.g. geographical origins, history, economic factors, incident review, emerging issues),

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active suppliers, supply chain (length, complexity, supply and demand arrangements), and existing

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control measures. The Nnext step is the design and review of mitigation strategies and their

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implementation. As soon as changes in the supply chain for an ingredient or a newly identified

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adulterant for an ingredient are known, which may impact the previously identified vulnerabilities,

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the entire process must be reassessed in order to ensure its continued effectiveness. For more

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details on the Food Fraud iInitial Screening (FFIS) visit Spink et al. [13] and COSO literature [14]. It is

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notable that GFSI compliance already requires a food fraud vulnerability assessment and mitigation

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plan, which forces food companies into action. More recently, SSAFE, developed in cooperation with

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PwC, Wageningen University, Vrije Universiteit Amsterdam, and in consultation with food industry

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leaders around the world, developed a free-of-charge food fraud vulnerability assessment app, which

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is supports the GFSI requirements (for more details visit: www.pwc.com/foodfraud).

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Prediction of Ffood sSafety iIncidents and fFood fFraud

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In order to prevent incidents from happening in the future, vVarious researchers and risk managers

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have explored the possibilities to forecast food safety and food fraud incidents , including the timely

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identification of trends and eventsby monitoring trends and events. that might eventually give rise to

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food safety and food fraud incidents. Generally, various iInternational as well as and local

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developments may directly or indirectly influence the performance of food-producing systems, for

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example among them climate change, economy and trade shifts, but also human behaviourbehavior

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(e.g. bio terrorism or sabotage), and new technologies [15‒18]. The ten key drivers to of food safety and


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nutrition risks were recently identified recently in a scoping study on food safety and nutrition [19


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]:were: 1i) global economy and trade, 2ii) global cooperation and standard setting, 3iii) governance,

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4iv) demography and social cohesion, 5v) consumer attitudes and behaviour, 6vi) new food chain

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technologies, 7vii) competition for key resources, 8viii) climate change, 9ix) emerging food chain risks

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and disasters, and 10x) new agri-food chain structures.

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A holistic approach taking stock of the forces that act upon the food chain (from farm to fork) and

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their effect on food safety has been adopted by the FAO [20]. Such an approach has also been


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proposed to address climate induced food safety risks [16,; 18,; 21]. The FAO20 has adopted Aa holistic


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approach that includes a full host environment analysis of the whole entire food chain in which the

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driving forces of food safety risks are identified and assessed determined, including associated data

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sources from various origins. A similar approach has also been considered for climate induced food

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safety risks.16,18,21

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Application of the holistic approach in a working system to identify known and emerging food safety

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risks needs a model that links all drivers and their dependencies. It also needs to connect , as well as

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underlying databases from various sources and origins, and preferably allows scenario studies. A

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suitable model should access data on the drivers, process these data, and perform calculations to

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provide predictions on emerging food safety and food fraud risks, preferably in real-time, data on the

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drivers, process these data, and perform calculations in order to provide predictions on (emerging)

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food safety and food fraud risks. Up to nowTo date, no modelling approach or system has been

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developed for the food production chain that is able to combine take into account underlying

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databases, interactions, and feed-back loops of the drivers, as required as encountefor red in a

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holistic approach. One promising approach, however, is utilizing a Bayesian Network (BN) approach.

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Novel food fraud predication approaches should consist of multi-source/multi-domain strategies

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combining relevant information from the food business information domain, the competent

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authorities information domain, as well as the public domain (see Figure 3). That kind of big data

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approach will most likely be able to provide the largest data pool for food fraud prediction models.

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Data access rights/data ownership as well as streamlining data plausibility will be the biggest legal

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challenge for a successful food safety risk management. Ultimately, global information contracts

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between all involved stakeholders involved, agri-food business, competent authorities, and the

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public domain alike, are foreseen as a long term requirement for food fraud predictions.

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Bayesian nNetwork

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BNs are a class of probabilistic models originating from Bayesian statistics and decision theory in

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addition to graph theory [22,; 23]. The structure of a BN consists ofcontains nodes (i.e., random

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variables) connected by directed arcs reflecting a dependence structure between the nodes. BNs are

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applied in many applicationsfields, such as hazard and risk assessment [24,; 25], fraud detection [26,; 27],

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accident prevention [28], and food fraud prediction [29,; 30]., nanomaterials [31,; 32], and medical image

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analysis [33]. BNs have the ability to integrate different data sources and types, such as expert

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knowledge, measurement data, and feedback experience

[341


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] using Bayes theorem.


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Case Sstudy hHerbs & sSpices

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Recently, Bouzembrak et al. [3533] developed a BN model in order to predict the presence of food

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safety hazards in herbs and spices to guide risk-based monitoring (prediction accuracy of 85%). This

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suggested approach consisted of a 3-step-process: 1(i) data collection on the main factors (hazards,

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product, country of origin) from using different data sources among which including KAP (Dutch

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national monitoring program for chemical contaminations in food and feed) and the RASFF database

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, the Dutch national monitoring program for chemical contaminations in food and feed (KAP),

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Eurostat, the EU pesticide database and legislation; 2(ii) construct a BN model including nodes,

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arrows, states, and the parameters for each node in the form of conditional probability tables (CPTs);

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and 3(iii) validate the model using an independent dataset.

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A summary of the most common hazards, products, countries of origin reported for herbs and spices

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is shown in Table 1.

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Table 1: Most common hazards reported for herbs and spices in the KAP and RASFF database. The

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numbers in bracket indicate the number of incidents.

Hazard category:

Affected products:

Countries of origin:

1. Mycotoxins (1422)

1. Paprika (398)

RASFF (2005-2014)

2. Pesticides (528)

2. Chili pepper (330)

3. Pathogens (459)

3. Curry leaves (279)

4. Composition (399)

1. India (32%) 2. Thailand (12%) 3. Vietnam (5%) KAP (2005-2011) 1. India (23%) 2. Netherlands (11%) 3. Indonesia (8%)

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Overall, the most common hazards recorded for herbs and spices in RASFF (2005-2014) and KAP

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(2005-2011) were mycotoxins (1422), pesticides (528), pathogens (459), and composition (399) and

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on product basis paprika (398), chili pepper (330), and curry leaves (279). Considering the countries

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of origin, the most frequently reported origin in RASFF and KAP was India (32%), followed by Thailand

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(12%) and Vietnam (5%), while in KAP India (23%) was first, followed by the Netherlands (11%) and

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Indonesia (8%). It is noticeable that a lack of consistency in the terminology used in the used data

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sources occurred. On the other hand, the The BN model could help to target sampling and analyses 12



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of products based on hazard prediction e.g. decide if investigations should be focused at market level

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or border inspectioncan also be applied to identify the products, hazards, and countries of origin to

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be sampled at the market level (. As reported by Bouzembrak et al. [35],). the monitoring plan

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depends on the point in the supply chain, at which the product is sampled. For instance, at a border

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inspection, the inspection should focus on curry, curry leaves, and chili pepper imported from India,

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whereas at the market level, the inspection should focus more on paprika, chili pepper, and nutmeg

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imported from India and Thailand.


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Conclusion & Expectations

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Over the last decade the focus of food safety developments around the world shifted from the

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traditional food safety HACCP principle to food defense (after 09/11 in the USA) and most recently to

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food fraud prevention. The latter is heavily pushed forward on in the global agenda of food safety

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due to substantial regulatory acitivities in the USA, China, and Europe, but also due to the

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engagement of the global agri -food business in order to prevent food fraud. Joint efforts making use

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of the food business information domains and the competent authorities’ information domains at

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the global level could contribute to minimize economical losses and to improve consumers’

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confidence in food safety and quality (Figure 3). Closer international cooperations between national

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authorities at the strategic and operational level as well as improved and enlarged IT-based data

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exchange systems along the entire agri -food chain would contribute to that goal. Cooperations are

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considered as prerequisites in order to evolve food safety in the 21st century.

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Digital challenges will be the development of novel and reliable food fraud predication approaches

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applying e.g. artificial neural networks and/or probabilitisticprobabilistic models, such as a Bayesian

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nNetwork, enabling adequate and timely ressource allocations within the food control community as

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well as within the agri -food business. Ultimatively, big data solutions will also require significant

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changes in the education and qualification of future employees of competent authorities and the

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agri -food business alike. being part of an industry 4.0 environment. 13


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Acknowledgement

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The author would like to thank Prof. Martin Holle (Hamburg University of Applied Sciences, Germany)

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for fruitfull discussions on legal aspects.

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References

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1. Tähkäpää, S.; Maijala, R.; Korkeala, H.; Nevas, M. Patterns of food frauds and adulterations

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reported in the EU rapid alert system for food and feed and in Finland. Food Control 2015,

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47, 175‒184.

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Networks in the development of herbs and spices sampling monitoring system. Food Control

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Table 1: Most common hazards reported for herbs and spices in the KAP and RASFF database. The

Formatted: Left: 0.98", Bottom: 0.79", Width: 8.27", Height: 11.69"

numbers in bracket indicate the number of incidents.

Hazard category:

Affected products:

Countries of origin:

Formatted Table

1. Mycotoxins (1422)

1. Paprika (398)

RASFF (2005-2014)

2. Pesticides (528)

2. Chili pepper

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3. Pathogens (459) 4. Composition (399)

(330) 3. Curry leaves (279)

1. India (32%) 2. Thailand (12%)


3. Vietnam (5%) KAP (2005-2011)


1. India (23%) 2. Netherlands (11%) 3. Indonesia (8%)


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FIGURE 1: The AAPVL-concept for an automated computer-aided identification and evaluation of potentially non-compliant food products traded via ecommerce (A. Krewinkel unpublished data).

20



Page 24 of 25

FIGURE 2: Food Fraud Vulnerability Assessment following an Enterprise Risk Management (ERM) and PDCA approach (J. Fritsche unpublished data).

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Page 25 of 25


FIGURE 3: Multi-source/multi-domain approach for the prevention of food fraud (J. Fritsche unpublished data).

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