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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: 15 November 2026 | Viewed by 7845

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 250 words) can be sent to the Editorial Office for assessment.

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-anonymized 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 (6 papers)

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Research

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19 pages, 1394 KB  
Article
Can Citizen Science Be a Key Factor in the Fight Against Mislabeling? Discovering What Squid Is on the Plate
by Marta Muñoz-Colmenero, Marta Vargas-Ramírez, Pedro Perdiguero, Isabel Ballesteros, Beatriz Beroiz, Félix Gil, Rosario Linacero and Jose Luis Horreo
Foods 2026, 15(10), 1690; https://doi.org/10.3390/foods15101690 - 12 May 2026
Viewed by 358
Abstract
Governments invest much effort trying to control food markets as well as to protect consumer rights, but resource limitations and the complexity of the scenario make it a challenging task. To overcome this, the use of Citizen Science could potentially be a powerful [...] Read more.
Governments invest much effort trying to control food markets as well as to protect consumer rights, but resource limitations and the complexity of the scenario make it a challenging task. To overcome this, the use of Citizen Science could potentially be a powerful tool to fight against food fraud. We have researched this through the assessment of mislabeling for squids of Loligo genus, one of the most appreciated and consumed foods in Spain and Europe, with a big information gap regarding their mislabeling status. We involved volunteers to collect squid samples at Spanish restaurants, genetically identifying them later. Thanks to participants, sampling was highly successful, with 93.55% of the samples recovered and very similar mislabeling values being found whether samples were collected by experts or volunteers. Citizen Science provided high territorial coverage (82% of Spanish autonomous communities) to gain a comprehensive view of Loligo mislabeling in Spanish restaurants, its magnitude (91.59%) and consumer preferences, demonstrating a huge potential of the use of Citizen Science in traceability studies. Interestingly, mislabeling was equally present in both sandwiches and plates, was higher in inland localities than on the coast, and was higher in cheaper products. Participants reported a greater understanding of the concept of mislabeling after their participation and expressed high interest in their involvement in scientific projects in the future. They also emphasized the importance of fostering a close relationship between science and society. Full article
(This article belongs to the Special Issue Anti-Food Fraud: Technologies in Food Safety, Quality and Traceability)
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19 pages, 3282 KB  
Article
Rapid Detection of Black Pepper Adulteration with Endogenous and Exogenous Materials: Assessment of Benchtop and Handheld Infrared Spectrometers
by Paul Rentz, Alina Mihailova, Horacio Heinzen, Martine Bergaentzlé, Elisa Ruhland, Marivil D. Islam, Islam Hamed, Christina Vlachou, Simon Kelly, Said Ennahar and Dalal Werner
Foods 2026, 15(4), 754; https://doi.org/10.3390/foods15040754 - 19 Feb 2026
Viewed by 889
Abstract
Black pepper is the most widely used spice crop globally and has significant economic value, making it a target for economically motivated adulteration. A wide range of organic and inorganic bulking materials has been used as adulterants in black pepper. Development of rapid [...] Read more.
Black pepper is the most widely used spice crop globally and has significant economic value, making it a target for economically motivated adulteration. A wide range of organic and inorganic bulking materials has been used as adulterants in black pepper. Development of rapid non-targeted screening methods for use at different stages of the black pepper supply chain is extremely important for the identification and prevention of evolving fraudulent practices. This study has assessed the potential of benchtop Fourier Transform infrared with attenuated total reflectance (FTIR-ATR), benchtop Fourier Transform near-infrared (FT-NIR), and two handheld NIR spectrometers, coupled with chemometrics, for the discrimination of black pepper (Piper nigrum), pepper from other species and genera (non-Piper nigrum) and a broad range (n = 27) of endogenous and exogenous adulterants. Spiked samples were prepared to imitate pepper adulteration with seven different adulterants at five levels of adulteration (5%, 25%, 50%, 75%, 95% w/w). Orthogonal partial least squares discriminant analysis (OPLS-DA) achieved 100% total prediction accuracy for both FTIR-ATR and FT-NIR in differentiating authentic Piper nigrum and adulterant samples. The handheld microNIR 1700ES resulted in a 91.30% correct classification rate, while the SCiO model achieved 86.96% prediction accuracy. Detection of black pepper adulteration with multiple adulterants was performed using data-driven soft independent modelling of class analogy (DD-SIMCA). The highest performance of the DD-SIMCA model was achieved by FTIR-ATR (100% sensitivity and 100% specificity) followed by FT-NIR (98% sensitivity and 99% specificity). The handheld microNIR 1700ES resulted in 95% sensitivity and 90% specificity. This study demonstrated that FTIR-ATR and FT-NIR, coupled with DD-SIMCA, can effectively detect black pepper adulteration with multiple endogenous and exogenous adulterants. The handheld NIR (microNIR1700ES) clearly demonstrated the potential for rapid and effective verification of Piper nigrum authenticity outside the laboratory. Full article
(This article belongs to the Special Issue Anti-Food Fraud: Technologies in Food Safety, Quality and Traceability)
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19 pages, 6041 KB  
Article
Integrating RPA-LFD and TaqMan qPCR for Rapid On-Site Screening and Accurate Laboratory Identification of Coilia brachygnathus and Coilia nasus in the Yangtze River
by Yu Lin, Suyan Wang, Min Zhang, Na Wang, Hongli Jing, Jizhou Lv and Shaoqiang Wu
Foods 2025, 14(20), 3484; https://doi.org/10.3390/foods14203484 - 13 Oct 2025
Cited by 1 | Viewed by 956
Abstract
Accurate differentiation between Coilia brachygnathus and Coilia nasus is imperative for the effective management of fisheries, the conservation of aquatic ecosystems, and the mitigation of commercial fraud. Current morphological identification remains challenging due to their high morphological similarity—particularly for processed samples—while conventional molecular [...] Read more.
Accurate differentiation between Coilia brachygnathus and Coilia nasus is imperative for the effective management of fisheries, the conservation of aquatic ecosystems, and the mitigation of commercial fraud. Current morphological identification remains challenging due to their high morphological similarity—particularly for processed samples—while conventional molecular methods often lack the speed or specificity required for field applications or high-throughput screening. In this study, a novel integrated approach was developed and validated, combining TaqMan quantitative real-time PCR (qPCR). for precise genotyping of C. brachygnathus and C. nasus with Recombinase Polymerase Amplification coupled with Lateral Flow Dipstick (RPA-LFD) for rapid on-site screening. First, species-specific RPA-LFD assays were designed to target the mitochondrial COI gene sequence. This enabled visual detection within 10 min at 37 °C, with a sensitivity of 102 copies/μL, and required no complex equipment. A dual TaqMan MGB qPCR assay was further developed by validating stable differentiating SNPs (chr21:3798155, C/T) between C. brachygnathus and C. nasus, using FAM/VIC dual-labeled MGB probes. Results showed that this assay could distinguish the two species in a single tube: for C. brachygnathus, Ct values in the FAM channel were significantly earlier than those in the VIC channel (ΔCt ≥ 1), with a FAM detection limit of 125 copies/reaction; for C. nasus, only VIC channel amplification was observed, with a detection limit as low as 12.5 copies/reaction. Validation with 171 known tissue samples demonstrated 100% concordance with expected species identities. This integrated approach effectively combines the high accuracy and quantitative capacity of TaqMan qPCR for confirmatory laboratory genotyping with the speed, simplicity, and portability of RPA-LFD for initial field or point-of-need screening. This reliable, efficient, and user-friendly technique provides a powerful tool for resource management, biodiversity monitoring, and ensuring the authenticity of high-quality C. brachygnathus and C. nasus. Full article
(This article belongs to the Special Issue Anti-Food Fraud: Technologies in Food Safety, Quality and Traceability)
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19 pages, 3263 KB  
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
Cited by 1 | Viewed by 1715
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 KB  
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
Cited by 4 | Viewed by 1680
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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Review

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21 pages, 1209 KB  
Review
Intelligent Discrimination of Grain Aging Using Volatile Organic Compound Fingerprints and Machine Learning: A Comprehensive Review
by Liuping Zhang, Jingtao Zhou, Guoping Qian, Shuyi Liu, Mohammed Obadi, Tianyue Xu and Bin Xu
Foods 2026, 15(2), 216; https://doi.org/10.3390/foods15020216 - 8 Jan 2026
Cited by 2 | Viewed by 1205
Abstract
Grain aging during storage leads to quality deterioration and significant economic losses. Traditional analytical approaches are often labor-intensive, slow, and inadequate for modern intelligent grain storage management. This review summarizes recent advances in the intelligent discrimination of grain aging using volatile organic compound [...] Read more.
Grain aging during storage leads to quality deterioration and significant economic losses. Traditional analytical approaches are often labor-intensive, slow, and inadequate for modern intelligent grain storage management. This review summarizes recent advances in the intelligent discrimination of grain aging using volatile organic compound (VOC) fingerprints combined with machine learning (ML) techniques. It first outlines the biochemical mechanisms underlying grain aging and identifies VOCs as early and sensitive biomarkers for timely determination. The review then examines VOC determination methodologies, with a focus on headspace solid-phase microextraction coupled with gas chromatography-mass spectrometry (HS-SPME-GC-MS), for constructing volatile fingerprinting profiles, and discusses related method standardization. A central theme is the application of ML algorithms, including Partial Least Squares Discriminant Analysis (PLS-DA), Support Vector Machines (SVM), Random Forest (RF), and Convolutional Neural Networks (CNN)) for feature extraction and pattern recognition in high-dimensional datasets, enabling effective discrimination of aging stages, spoilage types, and grain varieties. Despite these advances, key challenges remain, such as limited model generalizability, the lack of large-scale multi-source databases, and insufficient validation under real storage conditions. Finally, future directions are proposed that emphasize methodological standardization, algorithmic innovation, and system-level integration to support intelligent, non-destructive, real-time grain quality monitoring. This emerging framework provides a promising powerful pathway for enhancing global food security. Full article
(This article belongs to the Special Issue Anti-Food Fraud: Technologies in Food Safety, Quality and Traceability)
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