A 20-year analysis of reported food fraud in the global beef supply chain

Are online meatball restaurants in Indonesia committed to their declared Halal label?

Background and Aim

Halal restaurants participating in online food delivery services do not require halal certification. The Halal status of products through the Halal logo provides the consumer with information on the basis of which he decides to buy. Online transactions involve potential risks related to online processes, payment methods, and product quality. The aim of this study was to determine whether a declared Halal label is in accordance with the business processes implemented.

Materials and Methods

Halal authentication of Gofood's meatball partner products in Yogyakarta and Solo Raya determined the incompatibility of meatball ingredients. Sixty meatball samples were collected from Yogyakarta and 30 samples from Solo Raya. Halal certification test was carried out using the thermal cycle polymerase chain reaction method at Universitas Gadjah Mada Animal Husbandry Laboratory and the results were used to identify pork contamination in meatballs. The addition of pork or pork meatballs was used as a control.

Results

Eight meatball restaurants in the Solo Raya and Yogyakarta areas were found to be contaminated with pig DNA. The results of the tracing materials and processes, i.e., the grinding stage, are critical because all samples were supposed to be made from beef. It is known from interviews that contamination with pig DNA at the milling stage was accidental.

Conclusion

Restaurants that sell meatballs are committed to adhering to product labels that are 91.1% safe from pork contamination. The Halal and original beef labels were in accordance with their statements. This study highlights the concept of Halal authentication with traceability to overcome pork contamination in meat products.

Development of a real-time PCR protocol for the detection of chicken DNA in meat products

Food falsification is a pressing issue in today's food industry, with fraudulent substitution of costly ingredients with cheaper alternatives occurring globally. Consequently, developing straightforward and efficient diagnostic systems to detect such fraud is a top priority in scientific research. The aim of the work was to develop a test system and protocol for polymerase chain reaction (PCR) to detect in food products of animal origin the substitution of expensive meat raw materials for by-products of poultry processing. For this, real-time polymerase chain reaction (RT-PCR) was used, which allows determining the qualitative and quantitative substitution in raw and technologically prepared products. Other methods for detecting falsification - enzyme immunoassay (ELISA/ELISA) or express methods in the form of a lateral flow immunoassay are less informative. The extraction of nucleic acids for real-time polymerase chain reaction depends on the source matrix, with higher concentrations obtained from germ cells and parenchymal organs. Extraction from muscle and plant tissues is more challenging, but thorough grinding of these samples improves nucleic acid concentration by 1.5 times using DNA extraction kits. The selection of primers and fluorescent probes through GenBank and PCR Primer Design/DNASTAR software enables efficient amplification and identification of target chicken DNA fragments in various matrices.

Rapid identification of counterfeited beef using deep learning-aided spectroscopy: Detecting colourant and curing agent adulteration

The adulteration of meat products using colourants and curing agents has heightened concerns over food safety, thereby necessitating the development of advanced detection methods. This study introduces a deep-learning-based spectroscopic method for swiftly identifying counterfeit beef altered to appear fresh. The experiment involved 60 beef samples, half of which were artificially adulterated using a colouring solution. Despite meticulous analysis of the beef's colour attributes, no significant differences were observed between the fresh and adulterated samples. However, our method, utilising a 344-1040 nm spectral range, achieved a classification accuracy of 98.84%. To enhance practicality, we employed gradient-weighted class activation mapping and identified the 580-600 nm range as particularly influential for classification. Remarkably, even when we narrowed the input to the model to this spectral range, a high level of classification accuracy was maintained. To further validate the model's robustness and generalisability, we allocated 70 beef samples to an external validation set. Comparative performance analysis revealed that our model outperformed traditional machine learning algorithms, such as SVM and logistic regression, by 9.3% and 28.4%, respectively. Overall, this study offers invaluable insights for detecting counterfeited beef, thereby contributing to the preservation of meat product quality and integrity within the food industry.

Food fraud and mitigating strategies of UK food supply chain during COVID-19

Covid-19 had shown the vulnerability of the food supply chain and fraudsters may take advantage of the pandemic whilst the population needed a continuous supply of safe and quality food. The lack of monitoring and policing in the food supply chain may encourage fraudsters to upscale their operations. Previous studies had warned of a surge in fraudulent products due to COVID-19. This raised the question on whether food fraud had increased during the pandemic? This study aims to investigate food fraud during COVID-19 and how the food supply chain develops mitigating strategies against fraudulent activities. A mixed-method approach including survey and semi-structured interviews were conducted among UK food businesses. Two hundred and two agri-food businesses responded to the survey and 15 semi-structured interviews were conducted. The majority of the food businesses did not experience an increase of food fraud activities during COVID-19. Two thematic domains and ten sub-themes were identified from the data set. There was a heightened sense of anticipation and preparation for increased fraudulent activities during the pandemic. The main risk mitigating strategies included horizon scanning; developing and maintaining supplier relationship and assurance; understanding product characteristics, testing capabilities, conducting vulnerability assessments and training. Practical and cost-effective strategies for small and medium food businesses were recommended. This is the first empirical study on food fraud and mitigating strategies of the UK food supply chain during the pandemic. Our findings provide evidence for informing the policies and practices of the food regulatory authorities as well as best practices to protect the UK food supply chain against food fraud during exogenous shocks like COVID-19.

Implementation of relevant fourth industrial revolution innovations across the supply chain of fruits and vegetables: A short update on Traceability 4.0

Food Traceability 4.0 refers to the application of fourth industrial revolution (or Industry 4.0) technologies to ensure food authenticity, safety, and high food quality. Growing interest in food traceability has led to the development of a wide range of chemical, biomolecular, isotopic, chromatographic, and spectroscopic methods with varied performance and success rates. This review will give an update on the application of Traceability 4.0 in the fruits and vegetables sector, focusing on relevant Industry 4.0 enablers, especially Artificial Intelligence, the Internet of Things, blockchain, and Big Data. The results show that the Traceability 4.0 has significant potential to improve quality and safety of many fruits and vegetables, enhance transparency, reduce the costs of food recalls, and decrease waste and loss. However, due to their high implementation costs and lack of adaptability to industrial environments, most of these advanced technologies have not yet gone beyond the laboratory scale. Therefore, further research is anticipated to overcome current limitations for large-scale applications.

Meat food fraud risk in Chinese markets 2012-2021

Food fraud is a major concern worldwide, and the majority of cases include meat adulteration or fraud. Many incidences of food fraud have been identified for meat products both in China and abroad over the last decade. We created a meat food fraud risk database compiled from 1987 pieces of information recorded by official circular information and media reports in China from 2012 to 2021. The data covered livestock, poultry, by-products, and various processed meat products. We conducted a summary analysis of meat food fraud incidents by researching fraud types, regional distribution, adulterants and categories involved, categories and sub-categories of foods, risk links and locations, etc. The findings can be used not only to analyze meat food safety situations and study the burden of food fraud but also help to promote the efficiency of detection and rapid screening, along with improving prevention and regulation of adulteration in the meat supply chain markets.

Food Fraud Vulnerability Assessment in the Chinese Baijiu Supply Chain

As a representative of Chinese alcoholic drinks, baijiu has developed into a mass-consumption commodity. Its simple industrial chain makes it a suitable target for fraudsters. In order to understand the differences and potential factors of fraud vulnerability among groups at various levels, this study constructed a food fraud vulnerability assessment system for the Chinese baijiu supply chain based on routine activities theory. We examined the fraud vulnerability in the baijiu supply chain with data from 243 producers and 45 retailers by using the safe supply of affordable food everywhere (SSAFE) food fraud vulnerability assessment (FFVA) tool. The results indicate that fraud factors related to opportunities have an overall medium vulnerability, while those related to motivations and control measures have an overall medium-low vulnerability. In addition, there are significant differences in the perceived vulnerability of fraud factors across the supply chain. Moreover, retailers have overall higher fraud vulnerability in terms of opportunities and control measures than producers. The main reasons for the frequent occurrence of fraud in the baijiu industry are numerous technical opportunities, strong economic drivers, and insufficient control measures.

Pesticide Residues and Unauthorized Dyes as Adulteration Markers in Chilli Pepper and Tomato

To assess the contamination of processed chilli pepper and tomatoes, a report over the past four decades since the establishment of the Rapid Alert System for Food and Feed (RASFF) was retrieved and analysed. Out of the 887 notification reports assessed for eligibility, 446 were found regarding chilli pepper and tomato contamination. This study identified India as the country of origin with the highest number of reported cases relating to chilli pepper contamination. Italy and Türkiye were the countries with the highest number of reported cases regarding the exportation of adulterated tomatoes to other countries according to the RASFF report. Unauthorized dyes such as Sudan I, III, IV, orange II, rhodamine B, and para red were reported to have been detected in either chilli pepper or tomato in the supply chain. Almost all unauthorized dyes in this study were found to be more than the range (0.5 to 1 mg/kg) of the detection limit of Sudan dye and other related dyes using analytical methods set by the European Union. Unapproved pesticides by the European Union (EU) found in this study were acetamiprid, chlorothalonil, chlorpyrifos, dimethoate, methomyl, monocrotophos, omethoate, oxamyl, and thiophanate methyl. The present study indicates the persistence of chilli pepper and tomato contamination with harmful dyes and pesticide residues despite the ban on the use of certain chemicals in the food chain.

Hyperspectral Microscopy Technology to Detect Syrups Adulteration of Endemic Guindo Santo and Quillay Honey Using Machine-Learning Tools

Honey adulteration is a common practice that affects food quality and sale prices, and certifying the origin of the honey using non-destructive methods is critical. Guindo Santo and Quillay are fundamental for the honey production of Biobío and the Ñuble region in Chile. Furthermore, Guindo Santo only exists in this area of the world. Therefore, certifying honey of this species is crucial for beekeeper communities-mostly natives-to give them advantages and competitiveness in the global market. To solve this necessity, we present a system for detecting adulterated endemic honey that combines different artificial intelligence networks with a confocal optical microscope and a tunable optical filter for hyperspectral data acquisition. Honey samples artificially adulterated with syrups at concentrations undetectable to the naked eye were used for validating different artificial intelligence models. Comparing Linear discriminant analysis (LDA), Support vector machine (SVM), and Neural Network (NN), we reach the best average accuracy value with SVM of 93% for all classes in both kinds of honey. We hope these results will be the starting point of a method for honey certification in Chile in an automated way and with high precision.

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.

The Impact of Health Risk Perception on Blockchain Traceable Fresh Fruits Purchase Intention in China

This paper systematically investigates the impact of consumers’ health risk perceptions on the purchase intention of blockchain traceable fresh fruits in China. It uses online-survey data collected from four pilot cities that are part of the food traceability system in China. The ordinary least squares (OLS) and the ordered probit model was applied to examine the posited relationships. The results show that consumers’ health risk perception has a significant positive effect on the purchase intention of blockchain traceable fresh fruits. The stronger consumers’ health risk perception, the stronger their purchase intention of blockchain traceable fresh fruits. Likewise, heterogeneity exists among gender, age, income, and education in their corresponding effect of consumers’ health risk perception on blockchain traceable fresh fruit purchase intention. This suggests that male, high-aged, high-income and high-educated groups have a higher health risk perception, and therefore a higher purchase perception for blockchain traceable fresh fruits than female, low-aged, low-income and low-educated, respectively. Furthermore, family structure, consumers’ traceability cognition and purchase experience of traceable products affect the purchase intention of blockchain traceable fresh fruits. The study has several insights on the broader promotion, acceptance and development of the food traceability system and provides practical cues for policy and practice.

Hyperspectral imaging (HSI) for meat quality evaluation across the supply chain: Current and future trends

Meat products are particularly plagued by safety problems because of their complicated structure, various production processes and complex supply chains. Rapid and non-invasive analytical methods to evaluate meat quality have become a priority for the industry over the conventional chemical methods. To achieve rapid analysis of safety and quality parameters of meat products, hyperspectral imaging (HSI) is now widely applied in research studies for detecting the various components of different meat products, but its application in meat production and supply chain integrity as a quality control (QC) solution is still ambiguous. This review presents the fresh look at the current states of HSI research as both the scope and the applicability of the HSI in the meat quality evaluation expanded. The future application scenarios of HSI in the supply chain and the future development of HSI hardware and software are also discussed, by which HSI technology has the potential to enable large scale meat product testing. With a fully adapted for factory setting HSI, the inspection coverage can reliably identify the chemical properties of meat products. With the introduction of Food Industry 4.0, HSI advances can change the meat industry to become from reactive to predictive when facing meat safety issues.

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HSI has shown promising early signs in the non-destructive analysis of meat quality and safety.

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Hyperspectral imaging (HSI) is now widely applied in research studies for different meat products with the help of machine learning methods.

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With a fully adapted factory setting and robust machine learning of HSI, the inspection coverage can reach 100% of the target meat.

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HSI can change the meat industry to become from reactive to predictive when facing issues, this will be translated into fewer recalls, less meat fraud, and less waste.

An Analysis on E-Evaluation of Food Quality Traceability System

In recent years, China has obtained positive achievements in the construction of traceability systems for key products, such as edible agricultural products and food. However, problems such as complex situations, one-sided information, repeated system construction, and lack of qualification of information testing agencies still exist in food quality traceability. Based on the development features of the industry, this paper puts forward countermeasures and suggestions for the construction of a food quality traceability system.

Application of Blockchain Technology in Fresh Food Supply Chain Under COVID-19 Environment in China

Food safety is a vital issue of public and social concern. Since the outbreak of the COVID-19, frozen fresh food has become the hardest hit area for the spread of the COVID-19. In response to the opaque information, lack of trust, and difficulty in traceability in the fresh food supply chain, the article proposes blockchain technology to address the problem. This paper defines a blockchain technology use case and a quick reference guide to design a blockchain network for the food industry. It improves transparency throughout the supply chain and helps reconcile the documentation and required data with legislation authorities to import cold chain products to certify the quality of the final product. The fresh food supply chain framework can ensure integrity, authenticity, and supply chain information. This design is of great significance to ensure the traceability of the fresh food supply chain.

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.

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%).

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.