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Foods
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Anti-Food Fraud: Technologies in Food Safety, Quality and Traceability
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Anti-Food Fraud: Technologies in Food Safety, Quality and Traceability

A special issue of Foods (ISSN 2304-8158). This special issue belongs to the section "Food Analytical Methods".

Deadline for manuscript submissions: 3 September 2025 | Viewed by 994

Special Issue Editors

Dr. Yan Zhao

E-Mail Website
Guest Editor
Institute of Quality Standard & Testing Technology for Agro-Products, Key Laboratory of Agro-product Quality and Safety, Chinese Academy of Agricultural Sciences, Beijing 100081, China
Interests: food authenticity; stable isotope; rapid omics analysis; standardization of authenticity
Special Issues, Collections and Topics in MDPI journals
Dr. Yonghuan Yun

E-Mail Website
Guest Editor
College of Food Science and Technology, Hainan University, 58 Renmin Road, Haikou 570228, China
Interests: AI and chemometrics in food safety, quality, and traceability
Special Issues, Collections and Topics in MDPI journals
Dr. Kehong Liang

E-Mail Website
Guest Editor
Institute of Food and Nutrition Development, Ministry of Agriculture and Rural, Beijing, China
Interests: analysis and evaluation of the nutritional quality of agricultural products; quality analysis and origin tracing of agricultural products; multi-omics technologies

Special Issue Information

Dear Colleagues,

Food fraud now seriously hinders the development of food production, consumption, and management processes. Dilution, substitution, and counterfeit representations comprise some the different forms of common food fraud. At present, many technologies such as target analysis technology (stable isotope, mineral element, etc.),  non-target analysis technology (metabonomics, lipidomics, etc.), and biological identification methods have been used in the study of anti-food fraud. Currently, rapid and efficient analysis technology in combination with data screening is a new development trend in the food authenticity field. Spectroscopic technology, ambient ionization mass spectrometry, machine learning, and DNA-based technology have gradually been applied in food authenticity field due to their advantages of rapid analysis speed and simple operation.

Dr. Yan Zhao
Dr. Yonghuan Yun
Dr. Kehong Liang
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Foods is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2900 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • food authenticity
  • rapid identification
  • smart technology

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Published Papers (2 papers)

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Research

19 pages, 3263 KiB  
Article
Multi-Omics Mining of Characteristic Quality Factors Boosts the Brand Enhancement of the Geographical Indication Product—Pingliang Red Cattle
by Jing Liu, Yu Zhu, Xiaoxia Liu, Juan Zhang, Chuan Liu, Yan Zhao, Shuming Yang, Ailiang Chen and Jie Zhao
Foods 2025, 14(10), 1770; https://doi.org/10.3390/foods14101770 - 16 May 2025
Viewed by 285
Abstract
Pingliang Red Cattle, a renowned geographical indication product in China, is distinguished by its superior meat quality, yet the scientific basis for its unique attributes remains underexplored. This study integrated metabolomic and transcriptomic analyses to elucidate the biochemical and physiological factors underlying the [...] Read more.
Pingliang Red Cattle, a renowned geographical indication product in China, is distinguished by its superior meat quality, yet the scientific basis for its unique attributes remains underexplored. This study integrated metabolomic and transcriptomic analyses to elucidate the biochemical and physiological factors underlying the enhanced flavor, color stability, and tenderness of Pingliang Red Cattle beef compared to Qinchuan and Simmental cattle. Metabolomic profiling revealed significantly elevated levels of inosine monophosphate (IMP, 2.86–3.96× higher) and glutathione (GSH, 2.42–5.43× higher) in Pingliang Red Cattle, contributing to intense umami flavor and prolonged meat color retention. Notably, ergothioneine (EGT), a potent antioxidant, was identified for the first time in Pingliang Red Cattle beef, with concentrations 2.55× and 4.25× higher than in Qinchuan and Simmental, respectively. Transcriptomic analysis highlighted the upregulation of 21 tenderness-related genes (e.g., FABP3, PRDX6, CAST) and key enzymes in purine and glutathione metabolism pathways (e.g., PDE4D, ADSL, GGT1), correlating with meat tenderness and the improved meat quality. Additionally, Pingliang Red Cattle’s natural forage-rich diet and low-density rearing practices were critical in enhancing these traits. These findings provide a scientific foundation for Pingliang Red Cattle’s premium quality, offering actionable insights for GI product branding, quality optimization, and market competitiveness. The multi-omics approach established here serves as a paradigm for quality assessment and improvement of other GI agricultural products, bridging traditional reputation with molecular evidence. Full article
(This article belongs to the Special Issue Anti-Food Fraud: Technologies in Food Safety, Quality and Traceability)
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12 pages, 2710 KiB  
Article
Smartphone Video Imaging Combined with Machine Learning: A Cost-Effective Method for Authenticating Whey Protein Supplements
by Xuan Tang, Wenjiao Du, Weiran Song, Weilun Gu and Xiangzeng Kong
Foods 2025, 14(7), 1277; https://doi.org/10.3390/foods14071277 - 5 Apr 2025
Viewed by 470
Abstract
With the growing interest in health and fitness, whey protein supplements are becoming increasingly popular among fitness enthusiasts and athletes. The surge in demand for whey protein supplements highlights the need for cost-effective methods to characterise product quality throughout the food supply chain. [...] Read more.
With the growing interest in health and fitness, whey protein supplements are becoming increasingly popular among fitness enthusiasts and athletes. The surge in demand for whey protein supplements highlights the need for cost-effective methods to characterise product quality throughout the food supply chain. This study presents a rapid and low-cost method for authenticating sports whey protein supplements using smartphone video imaging (SVI) combined with machine learning. A gradient of colours ranging from purple to red is displayed on the front screen of a smartphone to illuminate the sample. The colour change on the sample surface is captured in a short video by the front-facing camera. Then, the video is split into frames, decomposed into RGB colour channels, and converted into spectral data. The relationship between video data and sample labels is established using machine learning models. The proposed method is tested on five tasks, including identifying 15 brands of whey protein concentrate (WPC), quantifying fat content and energy levels, detecting three types of adulterants, and quantifying adulterant levels. Moreover, the performance of SVI was compared to that of hyperspectral imaging (HSI), which has an equipment cost of around 80 times that of SVI. The proposed method achieves accuracies of 0.933 and 0.96 in WPC brand identification and adulterant detection, respectively, which are only around 0.05 lower than those of HSI. It obtains coefficients of determination of 0.897, 0.906 and 0.963 for the quantification of fat content, energy levels and milk powder adulteration, respectively. Such results demonstrate that the combination of smartphones and machine learning offers a low-cost and viable preliminary screening tool for verifying the authenticity of whey protein supplements. Full article
(This article belongs to the Special Issue Anti-Food Fraud: Technologies in Food Safety, Quality and Traceability)
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