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Foods
Special Issues
Food Fraud and Authenticity: Developments in Technologies
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Food Fraud and Authenticity: Developments in Technologies

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

Deadline for manuscript submissions: closed (15 February 2024) | Viewed by 3647

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 analysis; food authenticity; food traceability; mineral element; stable isotope; omics
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Duoyong Zhao

E-Mail Website
Co-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
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Haiyan Fu

E-Mail Website
Co-Guest Editor
School of Pharmaceutical Sciences, South-Central Minzu University, Wuhan 430074, China
Interests: food authentication and traceability; metabolomics; nanoeffect multivariate spectroscopy; food safety testing; chemometrics
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

In recent years, the problem of food adulteration has become increasingly rampant, seriously hindering the development of food production, consumption, and management.  Species fraud, dilution, substitution, unapproved enhancement, counterfeit, and origin mislabeling represent various forms of common food frauds. At present, numerous technologies, such as electronic label technology, chemical analysis methods, and biological identification methods, are employed in the study of food authenticity. The objects of food authenticity analysis include primary agricultural products, processed products and the key ingredients of food. Currently, rapid and efficient analysis technology is a primary development trend in the field of food authenticity. Spectroscopic technology, ambient ionization mass spectrometry, electronic sensors, and DNA-based technology have gradually been applied in the field of food authenticity due their rapid analysis speed and simple operation.

Considering the above, we invite researchers to submit their unpublished original manuscripts focusing on food authenticity and traceability. The subjects addressed by this Special Issue include, but are not limited to, the following:

  • Analytical solutions (targeted and untargeted methods) for food authenticity and traceability;
  • Food fingerprinting;
  • Detection of undeclared novel ingredients in foods;
  • Multi-methods approaches and data fusion;
  • Molecular analysis and biotechnological solutions (immunochemistry; PCR-related techniques; nucleic acids probes; micro- and nanosensors, MEMS);
  • Advanced statistical methods, post-analytical processing, artificial intelligence-based methods, and machine learning;
  • Information technologies such as Blockchain, coupled with analytical approaches.

Dr. Yan Zhao
Dr. Duoyong Zhao
Prof. Dr. Haiyan Fu
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 and traceability
  • targeted and untargeted methods
  • food fingerprinting
  • food omics
  • detection of undeclared novel ingredients in foods
  • multi-methods approaches and data fusion
  • molecular analysis and biotechnological solutions
  • PCR-related techniques
  • nucleic acids probes
  • artificial intelligence
  • machine learning
  • information technologies such as blockchain, coupled with analytical approaches

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

Published Papers (2 papers)

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Research

18 pages, 2980 KiB  
Article
Identification and Classification of Coix seed Storage Years Based on Hyperspectral Imaging Technology Combined with Deep Learning
by Ruibin Bai, Junhui Zhou, Siman Wang, Yue Zhang, Tiegui Nan, Bin Yang, Chu Zhang and Jian Yang
Foods 2024, 13(3), 498; https://doi.org/10.3390/foods13030498 - 4 Feb 2024
Cited by 5 | Viewed by 1513
Abstract
Developing a fast and non-destructive methodology to identify the storage years of Coix seed is important in safeguarding consumer well-being. This study employed the utilization of hyperspectral imaging (HSI) in conjunction with conventional machine learning techniques such as support vector machines (SVM), k-nearest [...] Read more.
Developing a fast and non-destructive methodology to identify the storage years of Coix seed is important in safeguarding consumer well-being. This study employed the utilization of hyperspectral imaging (HSI) in conjunction with conventional machine learning techniques such as support vector machines (SVM), k-nearest neighbors (KNN), random forest (RF), extreme gradient boosting (XGBoost), as well as the deep learning method of residual neural network (ResNet), to establish identification models for Coix seed samples from different storage years. Under the fusion-based modeling approach, the model’s classification accuracy surpasses that of visible to near infrared (VNIR) and short-wave infrared (SWIR) spectral modeling individually. The classification accuracy of the ResNet model and SVM exceeds that of other conventional machine learning models (KNN, RF, and XGBoost). Redundant variables were further diminished through competitive adaptive reweighted sampling feature wavelength screening, which had less impact on the model’s accuracy. Upon validating the model’s performance using an external validation set, the ResNet model yielded more satisfactory outcomes, exhibiting recognition accuracy exceeding 85%. In conclusion, the comprehensive results demonstrate that the integration of deep learning with HSI techniques effectively distinguishes Coix seed samples from different storage years. Full article
(This article belongs to the Special Issue Food Fraud and Authenticity: Developments in Technologies)
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16 pages, 2827 KiB  
Article
Towards Verifying the Imported Soybeans of China Using Stable Isotope and Elemental Analysis Coupled with Chemometrics
by Xiuwen Zhou, Beibei Xiong, Xiao Ma, Baohui Jin, Liqi Xie, Karyne M. Rogers, Hui Zhang and Hao Wu
Foods 2023, 12(23), 4227; https://doi.org/10.3390/foods12234227 - 23 Nov 2023
Viewed by 1559
Abstract
Verifying the geographical origin of soybeans (Glycine max [Linn.] Merr.) is a major challenge as there is little available information regarding non-parametric statistical origin approaches for Chinese domestic and imported soybeans. Commercially procured soybean samples from China (n = 33) and [...] Read more.
Verifying the geographical origin of soybeans (Glycine max [Linn.] Merr.) is a major challenge as there is little available information regarding non-parametric statistical origin approaches for Chinese domestic and imported soybeans. Commercially procured soybean samples from China (n = 33) and soybeans imported from Brazil (n = 90), the United States of America (n = 6), and Argentina (n = 27) were collected to characterize different producing origins using stable isotopes (δ2H, δ18O, δ15N, δ13C, and δ34S), non-metallic element content (% N, % C, and % S), and 23 mineral elements. Chemometric techniques such as principal component analysis (PCA), linear discriminant analysis (LDA), and BP–artificial neural network (BP-ANN) were applied to classify each origin profile. The feasibility of stable isotopes and elemental analysis combined with chemometrics as a discrimination tool to determine the geographical origin of soybeans was evaluated, and origin traceability models were developed. A PCA model indicated that origin discriminant separation was possible between the four soybean origins. Soybean mineral element content was found to be more indicative of origin than stable isotopes or non-metallic element contents. A comparison of two chemometric discriminant models, LDA and BP-ANN, showed both achieved an overall accuracy of 100% for testing and training sets when using a combined isotope and elemental approach. Our findings elucidate the importance of a combined approach in developing a reliable origin labeling method for domestic and imported soybeans in China. Full article
(This article belongs to the Special Issue Food Fraud and Authenticity: Developments in Technologies)
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