Food fraud of rejected imported foods in China in 2009–2019

Prevention of food fraud and fraud emulation among companies in the supply chain based on a social Co-governance framework

This study develops a three-party evolutionary game model among upstream raw material producers, midstream food producers, and downstream distributors in the food supply chain, and investigates food fraud and fraud emulation among companies in the same group based on a food safety social co-governance framework. Moreover, the equilibrium points are divided into four scenarios according to the number of groups of companies committing fraud in the supply chain and whether companies in the same group emulate each other's fraudulent behavior. The stability conditions of these scenarios are also discussed and verified by numerical simulation in MATLAB. The results show that the behavioral strategy choices of different groups of food companies in the supply chain are closely related to the level of social co-governance involving the government, market, and consumers. Government regulation, supervision between companies, and consumer reporting can all change companies' behavioral strategies. Although the level of fraud emulation among companies in the same group does not change their behavioral strategy choice, it affects the time it takes for their behavioral strategy to evolve to a stable state. Moreover, the level of social co-governance directly affects companies' behavioral strategy choices at different emulation levels.

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.

Theoretical and practical aspects of risk communication in food safety: A review study

Currently, food safety hazards have introduced as one of the most important threats to public health worldwide. Considering numerous crises in the field of food safety at global, regional, and national levels, and their impact on the physical and mental health of consumers, it is very vital to evaluate risk communication strategies in each country. Food safety risk communication (FSRC) aims to provide the means for individuals to protect their health from food safety risks and make informed decisions about food risks. The purpose of this study is to present FSRC as one of the key parts of risk analysis, its importance considering the prevalence of food contamination and recent crises related to food. Additionally, the stages of implementation of FSRC are mentioned. In FSRC, it is essential to comply with the principles and prerequisites. There are various strategies for FSRC nowadays. Different platforms for FSRC are rapidly evolving. Choosing and evaluating the appropriate strategy according to the target group, consensus of stakeholders, cooperation and coordination of risk assessors and risk managers have a significant impact in order to improve and implement FSRC.

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.

Variational Mode Decomposition Weighted Multiscale Support Vector Regression for Spectral Determination of Rapeseed Oil and Rhizoma Alpiniae Offcinarum Adulterants

The accurate prediction of the model is essential for food and herb analysis. In order to exploit the abundance of information embedded in the frequency and time domains, a weighted multiscale support vector regression (SVR) method based on variational mode decomposition (VMD), namely VMD-WMSVR, was proposed for the ultraviolet-visible (UV-Vis) spectral determination of rapeseed oil adulterants and near-infrared (NIR) spectral quantification of rhizoma alpiniae offcinarum adulterants. In this method, each spectrum is decomposed into K discrete mode components by VMD first. The mode matrix Uk is recombined from the decomposed components, and then, the SVR is used to build sub-models between each Uk and target value. The final prediction is obtained by integrating the predictions of the sub-models by weighted average. The performance of the proposed method was tested with two spectral datasets of adulterated vegetable oils and herbs. Compared with the results from partial least squares (PLS) and SVR, VMD-WMSVR shows potential in model accuracy.