Tools to combat food fraud - A gap analysis

Integrating different detection techniques and data analysis methods for comprehensive food authenticity verification

Traditional food testing methods, primarily confined to laboratory settings, are increasingly inadequate to detect covert food adulteration techniques. Hence, a crucial review of recent technological strides to combat food fraud is essential. This comprehensive analysis explores state-of-the-art technologies in food analysis, accentuating the pivotal role of sophisticated data processing methods and the amalgamation of diverse technologies in enhancing food authenticity testing. The paper assesses the merits and drawbacks of distinct data processing techniques and explores their potential synergies. The future of food authentication hinges on the integration of portable smart detection devices with mobile applications for real-time food analysis, including miniaturized spectrometers and portable sensors. This integration, coupled with advanced machine learning and deep learning for robust model construction, promises to achieve real-time, on-site food detection. Moreover, effective data processing, encompassing preprocessing, chemometrics, and regression analysis, remains indispensable for precise food authentication.

A review of the emerging technologies and systems to mitigate food fraud in supply chains

Food fraud has serious consequences including reputational damage to businesses, health and safety risks and lack of consumer confidence. New technologies targeted at ensuring food authenticity has emerged and however, the penetration and diffusion of sophisticated analytical technologies are faced with challenges in the industry. This review is focused on investigating the emerging technologies and strategies for mitigating food fraud and exploring the key barriers to their application. The review discusses three key areas of focus for food fraud mitigation that include systematic approaches, analytical techniques and package-level anti-counterfeiting technologies. A notable gap exists in converting laboratory based sophisticated technologies and tools in high-paced, live industrial applications. New frontiers such as handheld laser-induced breakdown spectroscopy (LIBS) and smart-phone spectroscopy have emerged for rapid food authentication. Multifunctional devices with hyphenating sensing mechanisms together with deep learning strategies to compare food fingerprints can be a great leap forward in the industry. Combination of different technologies such as spectroscopy and separation techniques will also be superior where quantification of adulterants are preferred. With the advancement of automation these technologies will be able to be deployed as in-line scanning devices in industrial settings to detect food fraud across multiple points in food supply chains.

Rapid and sensitive approaches for detecting food fraud: A review on prospects and challenges

Precise and reliable analytical techniques are required to guarantee food quality in light of the expanding concerns regarding food safety and quality. Because traditional procedures are expensive and time-consuming, quick food control techniques are required to ensure product quality. Various analytical techniques are used to identify and detect food fraud, including spectroscopy, chromatography, DNA barcoding, and inotrope ratio mass spectrometry (IRMS). Due to its quick findings, simplicity of use, high throughput, affordability, and non-destructive evaluations of numerous food matrices, NI spectroscopy and hyperspectral imaging are financially preferred in the food business. The applicability of this technology has increased with the development of chemometric techniques and near-infrared spectroscopy-based instruments. The current research also discusses the use of several multivariate analytical techniques in identifying food fraud, such as principal component analysis, partial least squares, cluster analysis, multivariate curve resolutions, and artificial intelligence.

NIR Sensing Technologies for the Detection of Fraud in Nuts and Nut Products: A Review

Food fraud is a major threat to the integrity of the nut supply chain. Strategies using a wide range of analytical techniques have been developed over the past few years to detect fraud and to assure the quality, safety, and authenticity of nut products. However, most of these techniques present the limitations of being slow and destructive and entailing a high cost per analysis. Nevertheless, near-infrared (NIR) spectroscopy and NIR imaging techniques represent a suitable non-destructive alternative to prevent fraud in the nut industry with the advantages of a high throughput and low cost per analysis. This review collects and includes all major findings of all of the published studies focused on the application of NIR spectroscopy and NIR imaging technologies to detect fraud in the nut supply chain from 2018 onwards. The results suggest that NIR spectroscopy and NIR imaging are suitable technologies to detect the main types of fraud in nuts.

Food fraud detection and reporting by food control officers in Finland

We studied food fraud detection and the reporting of suspected cases using a questionnaire survey and interviews with Finnish food control officers (FCOs). In total, 95 FCOs responded to the questionnaire, and 17 were interviewed. We found that even though many respondents had either suspected (69.2%) or detected (43.4%) food fraud or other food-related crime during the past five years, 46.8% thought they had no realistic chance of detecting food fraud during inspections. Challenges raised by the FCOs we interviewed included inadequate resources (8/17) and difficulties in inspecting documents or establishing their authenticity (14/17). Moreover, many interviewees highlighted difficulties in assessing whether to inform the police about a suspected case (7/17), and 62% (18/29) of respondents who had detected fraud had not reported it to the police. Training in food fraud detection, increased resources and guidelines on reporting suspected food fraud would improve food fraud detection and harmonize reporting.

Eggshell biometrics for individual egg identification based on convolutional neural networks

Individual egg identification technology has potential applications in breeding, product tracking/tracing, and anti-counterfeit. This study developed a novel method for individual egg identification based on eggshell images. A convolutional neural network-based model, named Eggshell Biometric Identification (EBI) model, was proposed and evaluated. The main workflow included eggshell biometric feature extraction, egg information registration, and egg identification. The image dataset of individual eggshell was collected from the blunt-end region of 770 chicken eggs using an image acquisition platform. The ResNeXt network was then trained as a texture feature extraction module to obtain sufficient eggshell texture features. The EBI model was applied to a test set of 1,540 images. The testing results showed that when an appropriate Euclidean distance threshold for classification was set (17.18), the correct recognition rate and the equal error rate reached 99.96% and 0.02%. This new method provides an efficient and accurate solution for individual chicken egg identification, and can be extended to eggs of other poultry species for product tracking/tracing and anti-counterfeit.

Development of Non-Targeted Mass Spectrometry Method for Distinguishing Spelt and Wheat

Food fraud, even when not in the news, is ubiquitous and demands the development of innovative strategies to combat it. A new non-targeted method (NTM) for distinguishing spelt and wheat is described, which aids in food fraud detection and authenticity testing. A highly resolved fingerprint in the form of spectra is obtained for several cultivars of spelt and wheat using liquid chromatography coupled high-resolution mass spectrometry (LC-HRMS). Convolutional neural network (CNN) models are built using a nested cross validation (NCV) approach by appropriately training them using a calibration set comprising duplicate measurements of eleven cultivars of wheat and spelt, each. The results reveal that the CNNs automatically learn patterns and representations to best discriminate tested samples into spelt or wheat. This is further investigated using an external validation set comprising artificially mixed spectra, samples for processed goods (spelt bread and flour), eleven untypical spelt, and six old wheat cultivars. These cultivars were not part of model building. We introduce a metric called the D score to quantitatively evaluate and compare the classification decisions. Our results demonstrate that NTMs based on NCV and CNNs trained using appropriately chosen spectral data can be reliable enough to be used on a wider range of cultivars and their mixes.

Handling multiblock data in wine authenticity by sequentially orthogonalized one class partial least squares

New approach to deal with food authentication by modelling methods based on data recorded from different sources is proposed and called OC-PLS, combines an orthogonalization step between the different data sets to eliminate redundant information followed by definition of an acceptance area for a target class by OC-PLS. The proposed method was evaluated in two case studies. The first study used a controlled scenario with simulated data. In the second case study, the approach was applied using UV-VIS and IR data, in order to differentiate Slovak Tokaj Selection wines of high quality from other lower market value wines from the Slovak Tokaj wine region. In both cases, better results were reached than when individual blocks of data were achieved. The proposed method proved to be effective in properly exploring common and distinct information in each data block. The best compromise between sensitivity and selectivity in the prediction step was achieved.

The 11 sins of seafood: Assessing a decade of food fraud reports in the global supply chain

Due to complex, valuable, and often extremely opaque supply chains, seafood is a commodity that has experienced a high prevalence of food fraud throughout the entirety of its logistics network. Fraud detection and prevention require an in-depth understanding of food supply chains and their vulnerabilities and risks so that food business operators, regulators, and other stakeholders can implement practical countermeasures. An analysis of historical criminality within a sector, product, or country is an important component and has not yet been conducted for the seafood sector. This study examines reported seafood fraud incidents from the European Union's Rapid Alert System for Food and Feed, Decernis's Food Fraud Database, HorizonScan, and LexisNexis databases between January 01, 2010 and December 31, 2020. Illegal or unauthorized veterinary residues were found to be the most significant issue of concern, with most reports originating from farmed seafood in Vietnam, China, and India. For internationally traded goods, border inspections revealed a significant frequency of reports with fraudulent or insufficient documentation, indicating that deceptive practices are picked up at import or export but are occurring further down the supply chain. Practices such as species adulteration (excluding veterinary residues), species substitution, fishery substitution, catch method fraud, and illegal, unreported, and unregulated fishing were less prevalent in the databases than evidenced in the scientific literature. The analysis demonstrates significant differences in outcomes depending on source and underlines a requirement for a standardized and rigorous dataset through which food fraud can be scrutinized to ensure enforcement, as well as industry and research resources are directed accurately. Practical Application: Levels of historic food fraud in a product, sector, supply chain node or geographic location provide an indication of historic criminality, the methods used and the location of reported frauds. This study provides an overview of historic levels of seafood fraud that can be used to inform seafood fraud prevention and mitigation activities by the food industry, regulators and other stakeholders.

Dietary Supplements Questioned in the Polish Notification Procedure upon the Basis of Data from the National Register of Functional Foods and the European System of the RASFF

Dietary supplements (DS) in the countries of the European Union falls within the scope of the food law. DS may, however, contain substances that are simultaneously applied in medicinal products as defined in the pharmaceutical law. The presence of such ingredients may cause problems with the product qualification. The phenomenon of applying such borderline ingredients in dietary supplements may require additional regulations, and ensuring them may be problematic. We conducted an analysis aiming to identify dishonest market practices resorted to by the producers and distributors of non-conforming dietary supplements. We examined mostly questioned DS and compared them with data from the RASFF system and registers of medicinal substances and pharmaceutical entities. The results show that some operators tend to re-notify the same products in response to the initiation of official control procedures. Products in the form of capsules or powders were the most common re-notifications within the 50–100 days. Based on the data obtained, it can be concluded that some entities are obliged to document the safety of the product or its compliance with the regulations, use the imperfection of the notification procedure, and re-notify the questioned product in order to keep it on the market despite potential non-compliance.

The "Hygiene Package": Analysis of Fraud Rates in Italy in the Period before and after Its Entry into Force

In violation of EU legislation, fraudulent activities in agri-food chains seek to make economic profits at the expense of consumers. Food frauds (FFs) often constitute a public health risk as well as a risk to animal and plant health, animal welfare and the environment. To analyze FFs in Italy during 1997-2020 with the aim of gaining observational insights into the effectiveness of the legislation in force and consequently of inspection activities, FFs were determined from official food inspections carried out by the Central Inspectorate of Quality Protection and Fraud Repression of Agri-food Products in 1997-2020. Inspected sectors were wine, oils and fats, milk and dairy products, fruit and vegetables, meat, eggs, honey, feeds and supplements, and seeds. Data show that the inspection activities have significantly improved in terms of sampling and fraud detection. However, a higher incidence of fraud involving the meat sector was observed. The obtained results demonstrate that there has not been a clear change of direction after the so-called "hygiene package" (food hygiene rules in the EU) came into force. Thus, more effective measures are needed to manage risk as well as new analytical solutions to increase the deterrence against meat adulteration and the rapid detection of fraud.

Strategic Priorities of the Scientific Plan of the European Research Infrastructure METROFOOD-RI for Promoting Metrology in Food and Nutrition

The pan-European distributed Research Infrastructure for Promoting Metrology in Food and Nutrition (METROFOOD-RI) has evolved in the frame of the European Strategy Forum on Research Infrastructures (ESFRI) to promote high-quality metrology services across the food chain. The METROFOOD-RI comprises physical facilities and electronic facilities. The former includes Reference Material plants and analytical laboratories (the ‘Metro’ side) and also experimental fields/farms, processing/storage plants and kitchen-labs (the ‘Food’ side). The RI is currently prepared to apply for receiving the European Research Infrastructure Consortium (ERIC) legal status and is organised to fulfil the requirements for operation at the national, European Union (EU) and international level. In this view, the METROFOOD-RI partners have recently reviewed the scientific plan and elaborated strategic priorities on key thematic areas of research in the food and nutrition domain to which they have expertise to contribute to meet global societal challenges and face unexpected emergencies. The present review summarises the methodology and main outcomes of the research study that helped to identify the key thematic areas from a metrological standpoint, to articulate critical and emerging issues and demands and to structure how the integrated facilities of the RI can operate in the first five years of operation as ERIC.

The fourth industrial revolution in the food industry-Part I: Industry 4.0 technologies

Climate change, the growth in world population, high levels of food waste and food loss, and the risk of new disease or pandemic outbreaks are examples of the many challenges that threaten future food sustainability and the security of the planet and urgently need to be addressed. The fourth industrial revolution, or Industry 4.0, has been gaining momentum since 2015, being a significant driver for sustainable development and a successful catalyst to tackle critical global challenges. This review paper summarizes the most relevant food Industry 4.0 technologies including, among others, digital technologies (e.g., artificial intelligence, big data analytics, Internet of Things, and blockchain) and other technological advances (e.g., smart sensors, robotics, digital twins, and cyber-physical systems). Moreover, insights into the new food trends (such as 3D printed foods) that have emerged as a result of the Industry 4.0 technological revolution will also be discussed in Part II of this work. The Industry 4.0 technologies have significantly modified the food industry and led to substantial consequences for the environment, economics, and human health. Despite the importance of each of the technologies mentioned above, ground-breaking sustainable solutions could only emerge by combining many technologies simultaneously. The Food Industry 4.0 era has been characterized by new challenges, opportunities, and trends that have reshaped current strategies and prospects for food production and consumption patterns, paving the way for the move toward Industry 5.0.

A Bayesian Approach to Predict Food Fraud Type and Point of Adulteration

Primary and secondary food processing had been identified as areas vulnerable to fraud. Besides the food processing area, other stages within the food supply chain are also vulnerable to fraud. This study aims to develop a Bayesian network (BN) model to predict food fraud type and point of adulteration i.e., the occurrence of fraudulent activity. The BN model was developed using GeNie Modeler (BayesFusion, LLC) based on 715 notifications (1979-2018) from Food Adulteration Incidents Registry (FAIR) database. Types of food fraud were linked to six explanatory variables such as food categories, year, adulterants (chemicals, ingredients, non-food, microbiological, physical, and others), reporting country, point of adulteration, and point of detection. The BN model was validated using 80 notifications from 2019 to determine the predictive accuracy of food fraud type and point of adulteration. Mislabelling (20.7%), artificial enhancement (17.2%), and substitution (16.4%) were the most commonly reported types of fraud. Beverages (21.4%), dairy (14.3%), and meat (14.0%) received the highest fraud notifications. Adulterants such as chemicals (21.7%) (e.g., formaldehyde, methanol, bleaching agent) and cheaper, expired or rotten ingredients (13.7%) were often used to adulterate food. Manufacturing (63.9%) was identified as the main point of adulteration followed by the retailer (13.4%) and distribution (9.9%).

Unauthorized Food Manipulation as a Criminal Offense: Food Authenticity, Legal Frameworks, Analytical Tools and Cases

Food fraud is a criminal intent motivated by economic gain to adulterate or misrepresent food ingredients and packaging. The development of a reliable food supply system is at great risk under globalization, but Food Business Operators (FBOs) have a legal obligation to implement and maintain food traceability and quality at all stages of food production, processing, and distribution. Incidents of food fraud have a strong negative impact on consumer confidence in the food industry. Therefore, local and international regulatory mechanisms are established to prevent or mitigate food fraud. This review brings new perspectives linking EU and US legislation, as well as new definitions and descriptions of the criminal aspect of food fraud incidents. It also describes certain new insights into the application of state-of-the-art methods and techniques that provide valuable tools for geographic, botanical, or other chemical markers of food authenticity. The review also provides an overview of the most common cases of food fraud worldwide from 2010 to 2020. Further research is needed to support the development of predictive models for innovative approaches to adulteration, especially when some valuable nutrients are replaced by toxic ingredients. A possible solution to minimize food fraud incidents is to increase the level of risk-based inspections, establish more productive monitoring and implementation of food protection systems in the supply chain, and implement better ingredient control and certification. National and international (e.g., regional) police offices for food fraud should be introduced, possessing knowledge and skills in food, food safety, food processing, and food products, as initial positive results have emerged in some countries.

Vegetable oils: Are they true? A point of view from ATR-FTIR, 1H NMR, and regiospecific analysis by 13C NMR

Problems related to oil authenticity make it difficult to obtain the benefits associated with each type of vegetable oil. Fraudulent practices have been revealed by several targeted and nontargeted methods. In this paper, spectroscopic techniques (FT-IR, ¹H NMR, and ¹³C NMR) were applied to determine the chemical profiles of 23 Brazilian commercial vegetable oils obtained from five different high-value aggregated matrices (andiroba, babassu, baru, castor, and sweet almond oils) and investigate their adulteration, by comparison with the corresponding reference samples. Each technique is useful for the particular information it provides: differences in free fatty acids by FT-IR; adulteration with omega-3-enriched oils by ¹H NMR, and adulteration of unsaturated-enriched oil with another unsaturated oil without linoleic acid by regiospecific analysis. Our findings highlight the importance of fusion-based methods in providing precise information for use in oil quality authentication.

MALDI-TOF Mass Spectrometry Applications for Food Fraud Detection

Chemical analysis of food products relating to the detection of the most common frauds is a complex task due to the complexity of the matrices and the unknown nature of most processes. Moreover, frauds are becoming more and more sophisticated, making the development of reliable, rapid, cost-effective new analytical methods for food control even more pressing. Over the years, MALDI-TOF MS has demonstrated the potential to meet this need, also due to a series of undeniable intrinsic advantages including ease of use, fast data collection, and capability to obtain valuable information even from complex samples subjected to simple pre-treatment procedures. These features have been conveniently exploited in the field of food frauds in several matrices, including milk and dairy products, oils, fish and seafood, meat, fruit, vegetables, and a few other categories. The present review provides a comprehensive overview of the existing MALDI-based applications for food quality assessment and detection of adulterations.

Quality aspects of European virgin olive oils with registered geographical indications: Emphasis on nutrient and non-nutrient bioactives

European virgin olive oil with geographical indications are strictly regulated and are of importance for the producing member states, Spain, Italy, Greece, Portugal, France, Slovenia and Croatia. These products are consumed locally, or within the European Union but are also exported worldwide. The chapter stresses on the importance of combining origin indications with other certifications or opportunities raising from European legislation in the agri-food sector so that to tighten consumer loyalty for this category of products. Emphasis is given to the richness of virgin olive oil in bioactive compounds that are already covered by nutritional and health claims (oleic acid, vitamin E, "polyphenols") and to those compounds that can be exploited in the future toward the same direction (squalene, oleanolic and maslinic acids).

Recent food safety and fraud issues within the dairy supply chain (2015-2019)

Milk and milk products play a vital role in diets around the globe. Due to their nutritional benefits there has been an increase in production and consumption over the past thirty years. For this growth to continue the safety and authenticity of dairy products needs to be maintained which is a huge area of concern. Throughout the process, from farm to processor, different sources of contamination (biological, chemical or physical) may occur either accidently or intentionally. Through online resources (the EU Rapid Alert System for Food and Feed (RASFF) and HorizonScan) safety and fraud data were collected from the past five years relating to milk and milk products. Cheese notifications were most frequently reported for both safety alerts (pathogenic micro-organisms) and fraud incidences (fraudulent documentation). Alongside the significant number of biological contaminations identified, chemical, physical and inadequate controls (in particular; foreign bodies, allergens, industrial contaminants and mycotoxins) were also found. Although the number of incidents were significantly smaller, these contaminants can still pose a significant risk to human health depending on their toxicity and exposure. Grey literature provided a summary of contamination and fraud issues from around the globe and shows its potential to be used alongside database resources for a holistic overview. In ensuring the integrity of milk during ever changing global factors (climate change, competition between food and feed and global pandemics) it is vital that safety and authenticity issues are continually monitored by industry, researchers and governing bodies.

Food Phenotyping: Recording and Processing of Non-Targeted Liquid Chromatography Mass Spectrometry Data for Verifying Food Authenticity

Experiments based on metabolomics represent powerful approaches to the experimental verification of the integrity of food. In particular, high-resolution non-targeted analyses, which are carried out by means of liquid chromatography-mass spectrometry systems (LC-MS), offer a variety of options. However, an enormous amount of data is recorded, which must be processed in a correspondingly complex manner. The evaluation of LC-MS based non-targeted data is not entirely trivial and a wide variety of strategies have been developed that can be used in this regard. In this paper, an overview of the mandatory steps regarding data acquisition is given first, followed by a presentation of the required preprocessing steps for data evaluation. Then some multivariate analysis methods are discussed, which have proven to be particularly suitable in this context in recent years. The publication closes with information on the identification of marker compounds.

Fraud in Animal Origin Food Products: Advances in Emerging Spectroscopic Detection Methods over the Past Five Years

Animal origin food products, including fish and seafood, meat and poultry, milk and dairy foods, and other related products play significant roles in human nutrition. However, fraud in this food sector frequently occurs, leading to negative economic impacts on consumers and potential risks to public health and the environment. Therefore, the development of analytical techniques that can rapidly detect fraud and verify the authenticity of such products is of paramount importance. Traditionally, a wide variety of targeted approaches, such as chemical, chromatographic, molecular, and protein-based techniques, among others, have been frequently used to identify animal species, production methods, provenance, and processing of food products. Although these conventional methods are accurate and reliable, they are destructive, time-consuming, and can only be employed at the laboratory scale. On the contrary, alternative methods based mainly on spectroscopy have emerged in recent years as invaluable tools to overcome most of the limitations associated with traditional measurements. The number of scientific studies reporting on various authenticity issues investigated by vibrational spectroscopy, nuclear magnetic resonance, and fluorescence spectroscopy has increased substantially over the past few years, indicating the tremendous potential of these techniques in the fight against food fraud. It is the aim of the present manuscript to review the state-of-the-art research advances since 2015 regarding the use of analytical methods applied to detect fraud in food products of animal origin, with particular attention paid to spectroscopic measurements coupled with chemometric analysis. The opportunities and challenges surrounding the use of spectroscopic techniques and possible future directions will also be discussed.