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Food Fraud as a Global Problem: Advanced Analytical Tools to Detect Species, Country of Origin and Adulterations: Second Edition

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A special issue of Foods (ISSN 2304-8158). This special issue belongs to the section "Food Quality and Safety".

Deadline for manuscript submissions: closed (20 December 2025) | Viewed by 13111

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Special Issue Editors

Dr. Hongyan Liu

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Research Center for Plants and Human Health, Institute of Urban Agriculture, Chinese Academy of Agricultural Sciences, National Agricultural Science and Technology Center, Chengdu 610213, China
Interests: food authentication; polyphenol; MS-based metabolomics; functional food; chemometrics
Special Issues, Collections and Topics in MDPI journals

Prof. Dr. Hongtao Lei

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Guangdong Provincial Key Laboratory of Food Quality and Safety, College of Food Science, South China Agricultural University, Guangzhou 510642, China
Interests: food authenticity; biosensor; chemometrics; food safety; food analysis; immunoassay; antibody engineering; hapten design
Special Issues, Collections and Topics in MDPI journals

Prof. Dr. Boli Guo

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Institute of Agro-Products Processing Science and Technology, Chinese Academy of Agricultural Sciences/Key Laboratory of Agro-Products Processing, Ministry of Agriculture, Beijing, China
Interests: cereal storage; cereal processing; quality control; cereal geographical traceability
Special Issues, Collections and Topics in MDPI journals

Prof. Dr. Duoyong Zhao

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Institute of Quality Standard & Testing Technology for Agro-Products, Xinjiang Academy of Agricultural Sciences, Ürümqi, China
Interests: food authenticity; food traceability; stable isotope
Special Issues, Collections and Topics in MDPI journals

Dr. Ren-You Gan

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Guest Editor

Singapore Institute of Food and Biotechnology Innovation (SIFBI), A*STAR, Singapore, Singapore
Interests: food authentication; phytochemical; food quality; bioactivity; analytical chemistry
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

This Special Issue follows on from the extraordinary success of Volume I (https://www.mdpi.com/journal/foods/special_issues/food_authenticity).

With the development of a globalized food market, food fraud has become a crucial issue across the world. The adulteration, substitution, and deliberate incorrect labeling of species and geographical origins of food are considered the main cases of food fraud. Some unscrupulous traders replace special local products with inferior or counterfeit products, substitute labeled food species with others, or extend the shelf life of food by using banned chemicals for economic profit, which can pose a great threat to human health.

Therefore, advanced analytical tools are needed as a means of scientific support against food fraud. Food authentication involves procedures to determine whether the product complies with its labeling, and whether it conforms to the legal standards and regulations that govern its consumption. The combination of appropriate techniques and statistical methods are considered to be powerful tools for identifying cases of food fraud.

This Special Issue aims to bring together the most recent research advances associated with the latest techniques and methods for identifying food fraud. We encourage the submission of original research articles, perspectives, opinion articles, and reviews that focus on, but are not limited to, the following topics: food fraud, stable isotopic ratios/elements, targeted and untargeted omics (LC-MS, GC-MS, etc.), spectroscopy (NIR, MIR, etc.), genetic analyses, AI technologies, and chemometrics/statistic methods.

Dr. Hongyan Liu
Prof. Dr. Hongtao Lei
Prof. Dr. Boli Guo
Prof. Dr. Duoyong Zhao
Dr. Ren-You Gan
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-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

geographical origin
food fraud
food adulteration
fingerprints
stable isotopic ratios
spectroscopy
omics
AI technologies

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Published Papers (8 papers)

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Research

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13 pages, 34410 KB

Open AccessCommunication

Quantitative Analysis of Biomarkers to Distinguish Between Korean and Chinese Mud Loaches

by Hyunsuk Kim, Junho Yang, Hyunji Lee, Hyeyoung Lee, Jiyoung Shin and Ji-Young Yang

Foods 2026, 15(2), 304; https://doi.org/10.3390/foods15020304 - 14 Jan 2026

Viewed by 516

Abstract

Mud loach (Misgurnus mizolepis) is a freshwater fish widely farmed in inland aquaculture owing to its nutritional value. However, failure to distinguish Chinese from Korean mud loach negatively affects the distribution economy and food safety regulation. Untargeted profiling was previously used to determine the origin of mud loaches, and N-acetylhistidine and anserine were selected as biomarker candidates. However, their quantitative verification and practical applicability for origin discrimination have not been thoroughly investigated. In this study, mud loaches of different geographical origins were analyzed using liquid chromatography-ultraviolet and liquid chromatography-tandem mass spectrometry to quantify the two metabolites, followed by statistical and receiver operating characteristic (ROC) analyses to evaluate their discriminative performance. Compared with Korean mud loaches, Chinese mud loaches showed significantly higher concentrations of both metabolites. The area under the curve values for N-acetylhistidine and anserine were 0.88 and 0.89, respectively, reflecting high sensitivity and specificity for discriminating between Korean and Chinese mud loaches. Cutoff values were established for reliably distinguishing the geographical origin of mud loaches. The established approach based on N-acetylhistidine and anserine can be used to determine the geographical origin of mud loach. Full article

(This article belongs to the Special Issue Food Fraud as a Global Problem: Advanced Analytical Tools to Detect Species, Country of Origin and Adulterations: Second Edition)

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16 pages, 2458 KB

Open AccessCommunication

Machine Learning and UHPLC–MS/MS-Based Discrimination of the Geographical Origin of Dendrobium officinale from Yunnan, China

by Tao Lin, Yanping Ye, Jiao Zhang, Jing Wang, Zhengxu Hu, Khine Zar Linn, Xinglian Chen, Hongcheng Liu, Zhenhuan Liu and Qinghua Yao

Foods 2025, 14(19), 3442; https://doi.org/10.3390/foods14193442 - 8 Oct 2025

Cited by 2 | Viewed by 1564

Abstract

A rapid targeted screening method for 22 compounds, including flavonoids, glycosides, and phenolics, in Dendrobium officinale was developed using UHPLC–MS/MS, demonstrating good linear correlation coefficients, precision, repeatability, and stability. D. officinale from the Guangnan and Maguan regions can be effectively classified into two distinct categories using PCA. In addition, OPLS-DA discriminant analysis enables clear separation between groups, with samples forming well-defined clusters. The 22 chemical components provide valuable origin-related information for D. officinale. The compounds with VIP values of >1 included eriodictyol, vanillic acid, protocatechuic acid, gentisic acid, and naringenin. The difference in naringenin content between D. officinale from the two production areas was minimal. By contrast, eriodictyol and vanillic acid were relatively abundant in D. officinale from Guangnan, while gentisic acid and protocatechuic acid were more prevalent in D. officinale from Maguan. The pathways with higher Kyoto Encyclopedia of Genes and Genomes enrichment were primarily associated with lipid metabolism and atherosclerosis, fluid shear stress and atherosclerosis, and nonalcoholic fatty liver disease. These findings suggest that D. officinale exhibits promising lipid-balancing properties and potential cardiovascular health benefits. Seven machine learning algorithms—Random Forest, XGBoost, Support Vector Machine, k-Nearest Neighbor, Backpropagation Neural Network, Random Tree, and CatBoost—demonstrated superior accuracy and precision in distinguishing D. officinale from the Guangnan and Maguan regions. The key compounds with higher weights—vanillic acid, chrysoeriol, trigonelline, isoquercitrin, gallic acid, 4-hydroxybenzaldehyde, eriodictyol, sweroside, apigenin, and homoeriodictyol—play a crucial role in model construction and the identification of D. officinale from the Guangnan and Maguan regions. The quantification of 22 compounds using UHPLC–MS/MS, combined with PCA, OPLS-DA, and machine learning, enables effective discrimination of D. officinale from these two Yunnan production areas. Full article

(This article belongs to the Special Issue Food Fraud as a Global Problem: Advanced Analytical Tools to Detect Species, Country of Origin and Adulterations: Second Edition)

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20 pages, 12217 KB

Open AccessArticle

Fc-Binding Cyclopeptide Induces Allostery from Fc to Fab: Revealed Through in Silico Structural Analysis to Anti-Phenobarbital Antibody

by Tao Zhou, Huiling Zhang, Xiaoting Yu, Kangliang Pan, Xiaojun Yao, Xing Shen and Hongtao Lei

Foods 2025, 14(8), 1360; https://doi.org/10.3390/foods14081360 - 15 Apr 2025

Cited by 2 | Viewed by 1899

Abstract

Allostery is a fundamental biological phenomenon that occurs when a molecule binds to a protein’s allosteric site, triggering conformational changes that regulate the protein’s activity. However, allostery in antibodies remains largely unexplored, and only a few reports have focused on allostery from antigen-binding fragments (Fab) to crystallizable fragments (Fc). But this study, using anti-phenobarbital antibodies—which are widely applied for detecting the potential health food adulterant phenobarbital—as a model and employing multiple computational methods, is the first to identify a cyclopeptide (cyclo[Link-M-WFRHY-K]) that induces allostery from Fc to Fab in antibody and elucidates the underlying antibody allostery mechanism. The combination of molecular docking and multiple allosteric site prediction algorithms in these methods identified that the cyclopeptide binds to the interface of heavy chain region-1 (CH₁) in antibody Fab and heavy chain region-2 (CH₂) in antibody Fc. Meanwhile, molecular dynamics simulations combined with other analytical methods demonstrated that cyclopeptide induces global conformational shifts in the antibody, which ultimately alter the Fab domain and enhance its antigen-binding activity from Fc to Fab. This result will enable cyclopeptides as a potential Fab-targeted allosteric modulator to provide a new strategy for the regulation of antigen-binding activity and contribute to the construction of novel immunoassays for food safety and other applications using allosteric antibodies as the core technology. Furthermore, graph theory analysis further revealed a common allosteric signaling pathway within the antibody, involving residues Q123, S207, S326, C455, A558, Q778, D838, R975, R1102, P1146, V1200, and K1286, which will be very important for the engineering design of the anti-phenobarbital antibodies and other highly homologous antibodies. Finally, the non-covalent interaction analysis showed that allostery from Fc to Fab primarily involves residue signal transduction driven by hydrogen bonds and hydrophobic interactions. Full article

(This article belongs to the Special Issue Food Fraud as a Global Problem: Advanced Analytical Tools to Detect Species, Country of Origin and Adulterations: Second Edition)

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20 pages, 7370 KB

Open AccessArticle

Explainable Deep Learning to Predict Kelp Geographical Origin from Volatile Organic Compound Analysis

by Xuming Kang, Zhijun Tan, Yanfang Zhao, Lin Yao, Xiaofeng Sheng and Yingying Guo

Foods 2025, 14(7), 1269; https://doi.org/10.3390/foods14071269 - 4 Apr 2025

Cited by 3 | Viewed by 1458

Abstract

In addition to its flavor and nutritional value, the origin of kelp has become a crucial factor influencing consumer choices. Nevertheless, research on kelp’s origin traceability by volatile organic compound (VOC) analysis is lacking, and the application of deep learning in this field remains scarce due to its black-box nature. To address this gap, we attempted to identify the origin of kelp by analyzing its VOCs in conjunction with explainable deep learning. In this work, we identified 115 distinct VOCs in kelp samples using gas chromatography coupled with ion mobility spectroscopy (GC-IMS), of which 68 categories were discernible. Consequently, we developed a comprehensible one-dimensional convolutional neural network (1D-CNN) model that incorporated 107 VOCs exhibiting significant regional disparities (p < 0.05). The model successfully discerns the origin of kelp, achieving perfect metrics across accuracy (100%), precision (100%), recall (100%), F1 score (100%), and AUC (1.0). SHapley Additive exPlanations (SHAP) analysis highlighted the impact of features such as 1-Octen-3-ol-M, (+)-limonene, allyl sulfide-D, 1-hydroxy-2-propanone-D, and (E)-2-hexen-1-al-M on the model output. This research provides deeper insights into how critical product features correlate with specific geographic information, which in turn boosts consumer trust and promotes practical utilization in actual settings. Full article

(This article belongs to the Special Issue Food Fraud as a Global Problem: Advanced Analytical Tools to Detect Species, Country of Origin and Adulterations: Second Edition)

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21 pages, 2506 KB

Open AccessArticle

Integrated Gel Electrophoresis and Mass Spectrometry Approach for Detecting and Quantifying Extraneous Milk in Protected Designation of Origin Buffalo Mozzarella Cheese

by Sabrina De Pascale, Giuseppina Garro, Silvia Ines Pellicano, Andrea Scaloni, Stefania Carpino, Simonetta Caira and Francesco Addeo

Foods 2025, 14(7), 1193; https://doi.org/10.3390/foods14071193 - 28 Mar 2025

Cited by 3 | Viewed by 1392

Abstract

Ensuring the authenticity of Mozzarella di Bufala Campana (MdBC), a Protected Designation of Origin (PDO) cheese, is essential for regulatory enforcement and consumer protection. This study evaluates a multi-technology analytical platform developed to detect adulteration due to the addition of non-buffalo milk or non-PDO buffalo milk in PDO dairy buffalo products. Peripheral laboratories use gel electrophoresis combined with polyclonal antipeptide antibodies for initial screening, enabling the detection of foreign caseins, including those originating outside the PDO-designated regions. For more precise identification, Matrix-Assisted Laser Desorption Ionization Time of Flight Mass Spectrometry (MALDI-TOF-MS) differentiates species by detecting proteotypic peptides. In cases requiring confirmation, nano-liquid chromatography coupled to electrospray tandem mass spectrometry (nano-LC-ESI-MS/MS) is used in central state laboratories for the highly sensitive detection of extraneous milk proteins in PDO buffalo MdBC cheese. On the other hand, analysis of the pH 4.6 soluble fraction from buffalo blue cheese identified 2828 buffalo-derived peptides and several bovine specific peptides, confirming milk adulteration. Despite a lower detection extent in the pH 4.6 insoluble fraction following tryptic hydrolysis, the presence of bovine peptides was still sufficient to verify fraud. This integrated proteomic approach, which combines electrophoresis and mass spectrometry technologies, significantly improves milk adulteration detection, providing a robust tool to face increasingly sophisticated fraudulent practices. Full article

(This article belongs to the Special Issue Food Fraud as a Global Problem: Advanced Analytical Tools to Detect Species, Country of Origin and Adulterations: Second Edition)

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16 pages, 1560 KB

Open AccessArticle

Challenges in Using the Official Italian Method to Detect Bovine Whey Proteins in Protected Designation of Origin Buffalo Mozzarella: A Proteomic Approach to Face Observed Limits

by Federica Della Cerra, Mariapia Esposito, Simonetta Caira, Andrea Scaloni and Francesco Addeo

Foods 2025, 14(5), 822; https://doi.org/10.3390/foods14050822 - 27 Feb 2025

Viewed by 1548

Abstract

This study critically examines the limitations of the official Italian methodology used for detecting bovine adulteration milk in Protected Designation of Origin (PDO) Mozzarella di Bufala Campana (MdBC). This method focuses on the whey fraction of cheese samples, which comprises about 1% of total MdBC proteins, and is based on a high-performance liquid chromatography (HPLC) quantification of the bovine β-lactoglobulin A (β-Lg A) as a marker. Here, we have demonstrated that this official methodology suffers from measurement inconsistencies due to its reliance on raw bovine whey standards, which fail to account for β-Lg genetic polymorphisms in real MdBC samples and protein thermal modifications during cheesemaking. To overcome these limitations, we propose a dual proteomics-based approach using matrix-assisted laser desorption ionization (MALDI-TOF) mass spectrometry (MS) and nano-HPLC-electrospray (ESI)−tandem mass spectrometry (MS/MS) analysis of MdBC extracted whey. MALDI-TOF-MS focused on identifying proteotypic peptides specific to bovine and buffalo β-Lg and α-lactalbumin (α-La), enabling high specificity for distinguishing the two animal species at adulteration levels as low as 1%. Complementing this, nano-HPLC-ESI-MS/MS provided a comprehensive profile by identifying over 100 bovine-specific peptide markers from β-Lg, α-La, albumin, lactoferrin, and osteopontin. Both methods ensured precise detection and quantification of bovine milk adulteration in complex matrices like pasta filata cheeses, achieving high sensitivity even at minimal adulteration levels. Accordingly, the proposed dual proteomics-based approach overcomes challenges associated with whey protein polymorphism, heat treatment, and processing variability, and complements casein-based methodologies already validated under European standards. This integrated framework of analyses focused on whey and casein fraction enhances the reliability of adulteration detection and safeguards the authenticity of PDO buffalo mozzarella, upholding its unique quality and integrity. Full article

(This article belongs to the Special Issue Food Fraud as a Global Problem: Advanced Analytical Tools to Detect Species, Country of Origin and Adulterations: Second Edition)

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21 pages, 5745 KB

Open AccessArticle

The Impact of Sample Quantity, Traceability Scale, and Shelf Life on the Determination of the Near-Infrared Origin Traceability of Mung Beans

by Ming-Ming Chen, Yan Song, Yan-Long Li, Xin-Yue Sun, Feng Zuo and Li-Li Qian

Foods 2024, 13(20), 3234; https://doi.org/10.3390/foods13203234 - 11 Oct 2024

Cited by 2 | Viewed by 1640

Abstract

This study aims to address the gap in understanding of the impact of the sample quantity, traceability range, and shelf life on the accuracy of mung bean origin traceability models based on near-infrared spectroscopy. Mung beans from Baicheng City, Jilin Province, Dorbod Mongol Autonomous, Tailai County, Heilongjiang Province, and Sishui County, Shandong Province, China, were used. Through near-infrared spectral acquisition (12,000–4000 cm⁻¹) and preprocessing (Standardization, Savitzky–Golay, Standard Normal Variate, and Multiplicative Scatter Correction) of the mung bean samples, the total cumulative variance contribution rate of the first three principal components was determined to be 98.16% by using principal component analysis, and the overall discriminatory correctness of its four origins combined with the K-nearest neighbor method was 98.67%. We further investigated how varying sample quantities, traceability ranges, and shelf lives influenced the discrimination accuracy. Our results indicated a 4% increase in the overall correct discrimination rate. Specifically, larger traceability ranges (Tailai-Sishui) improved the accuracy by over 2%, and multiple shelf lives (90–180–270–360 d) enhanced the accuracy by 7.85%. These findings underscore the critical role of sample quantity and diversity in traceability studies, suggesting that broader traceability ranges and comprehensive sample collections across different shelf lives can significantly improve the accuracy of origin discrimination models. Full article

(This article belongs to the Special Issue Food Fraud as a Global Problem: Advanced Analytical Tools to Detect Species, Country of Origin and Adulterations: Second Edition)

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Review

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32 pages, 1189 KB

Open AccessReview

Honey Fraud as a Moving Analytical Target: Omics-Informed Authentication Within a Multi-Layer Analytical Framework

by Dagmar Schoder

Foods 2026, 15(4), 712; https://doi.org/10.3390/foods15040712 - 14 Feb 2026

Viewed by 1056

Abstract

Honey fraud represents a persistent and analytically challenging form of food adulteration, driven by globalised supply chains, strong economic incentives and asymmetries in regulatory oversight and analytical capacity. Conventional physicochemical, spectroscopic and isotopic methods provide legally robust tools for routine control, yet increasingly struggle to detect sophisticated adulteration strategies that are compositionally optimised to mimic authentic honey profiles. These challenges are amplified in a global context, where heterogeneous enforcement landscapes and fragmented analytical infrastructures create exploitable vulnerabilities across international trade networks. This narrative review synthesises current knowledge on honey fraud typologies and critically evaluates established analytical approaches alongside emerging omics-based authentication strategies, including genomics, metabolomics, proteomics and microbiome profiling. Omics-based approaches extend authenticity assessment beyond single-marker paradigms by capturing multidimensional biological and compositional signatures, thereby improving sensitivity to subtle and system-aware fraud (i.e., adulteration strategies that adapt to prevailing analytical detection methods and regulatory thresholds) strategies. To maintain evidentiary clarity, this review explicitly distinguishes between analytically demonstrated vulnerabilities, technically feasible adulteration scenarios and fraud practices documented in regulatory or enforcement contexts. Advanced technology-driven strategies are therefore discussed as potential system-level risks rather than confirmed large-scale honey fraud cases. This differentiation not only safeguards evidentiary precision but also highlights the structural limits of purely analytical solutions. Beyond analytical performance, honey authentication is framed as a systemic challenge embedded in global food systems. This review highlights the need for integrated, data-driven and scalable authentication frameworks that align analytical innovation with reference harmonisation, governance structures and international regulatory cooperation to support resilient and globally robust honey authenticity control. Full article

(This article belongs to the Special Issue Food Fraud as a Global Problem: Advanced Analytical Tools to Detect Species, Country of Origin and Adulterations: Second Edition)

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