Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
7.4
4.7
Journals
Foods
Special Issues
Food Fraud as a Global Problem: Advanced Analytical Tools to...
share announcement

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

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

Deadline for manuscript submissions: 30 June 2025 | Viewed by 3840

Special Issue Editors

Dr. Hongyan Liu

E-Mail Website
Guest Editor
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

E-Mail Website
Guest Editor
Guangdong Province Key Laboratory of Food Quality and Safety/National-Local Joint Engineering Research Center for Machining and Safety of Livestock and Poultry Products, 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

E-Mail Website
Guest Editor
Comprehensive Utilization Laboratory of Cereal and Oil Processing, Institute of Food Science and Technology, Chinese Academy of Agriculture Sciences, Ministry of Agriculture and Rural Affairs of the People Republic of China, Beijing 100193, China
Interests: cereal storage; cereal processing; quality control; cereal geographical traceability
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Duoyong Zhao

E-Mail Website
Guest Editor
Institute of Quality Standard & Testing Technology for Agro-Products, Xinjiang Academy of Agricultural Sciences, Urumqi, China
Interests: food authenticity; food traceability; stable isotope
Dr. Ren-You Gan

grade E-Mail Website
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 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

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

Benefits of Publishing in a Special Issue

  • Ease of navigation: Grouping papers by topic helps scholars navigate broad scope journals more efficiently.
  • Greater discoverability: Special Issues support the reach and impact of scientific research. Articles in Special Issues are more discoverable and cited more frequently.
  • Expansion of research network: Special Issues facilitate connections among authors, fostering scientific collaborations.
  • External promotion: Articles in Special Issues are often promoted through the journal's social media, increasing their visibility.
  • Reprint: MDPI Books provides the opportunity to republish successful Special Issues in book format, both online and in print.

Further information on MDPI's Special Issue policies can be found here.

Published Papers (5 papers)

Download All Papers
Order results
Result details
Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:

Research

20 pages, 12217 KiB  
Article
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
Viewed by 392
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 [...] Read more.
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)
►▼ Show Figures

Figure 1

20 pages, 7370 KiB  
Article
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
Viewed by 309
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 [...] Read more.
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)
►▼ Show Figures

Graphical abstract

21 pages, 2506 KiB  
Article
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
Viewed by 293
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 [...] Read more.
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)
►▼ Show Figures

Figure 1

16 pages, 1560 KiB  
Article
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 590
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 [...] Read more.
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)
►▼ Show Figures

Figure 1

21 pages, 5745 KiB  
Article
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 1 | Viewed by 897
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 [...] Read more.
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)
►▼ Show Figures

Graphical abstract

Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:
 
Displaying articles 1-5
Back to TopTop