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Foods
Special Issues
Chemometrics in Food Authenticity and Quality Control
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Chemometrics in Food Authenticity and Quality Control

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

Deadline for manuscript submissions: 25 October 2026 | Viewed by 1701

Special Issue Editors

Dr. Pao Li

E-Mail Website
Guest Editor
College of Food Science and Technology, Hunan Agricultural University, Changsha, China
Interests: chemometric methods; near-infrared spectral analysis; gas chromatography–mass spectrometry; fruit and vegetable quality control
Prof. Dr. Xihui Bian

E-Mail Website
Guest Editor
School of Pharmaceutical Sciences, Tiangong University, Tianjin, China
Interests: chemometric methods; rapid nondestructive detection of edible oil; quality control of traditional Chinese medicine; near-infrared spectral analysis; Raman spectral analysis
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

In recent years, food authenticity and quality control have emerged as critical concerns, especially within the research community. Chemometrics, as a powerful analytical tool for extracting meaningful information from complex datasets, is revolutionizing the field of food analysis. Authenticity and quality control models require optimized analytical methodologies, significant sampling, and validation of the built model. Noticing the rapid expansion in studies concerning chemometrics in food analysis, we are announcing a Special Issue, entitled “Chemometrics in Food Authenticity and Quality Control”, in the journal Foods. Topics of interest include, but are not limited to, the following: machine learning, multivariate analysis, spectral data (e.g., NIR, Raman, and MS) analysis, and real-time monitoring solutions for food authenticity and quality control. In this Special Issue, original research articles and reviews are welcome. 

We look forward to receiving your contributions.

Dr. Pao Li
Prof. Dr. Xihui Bian
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

  • chemometrics
  • machine learning
  • spectral analysis
  • spectral imaging
  • fingerprint analysis
  • hyphenated methods
  • authenticity assessment
  • quality control

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Published Papers (2 papers)

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Research

15 pages, 663 KB  
Article
Optimization of SERS Detection for Sulfathiazole Residues in Chicken Blood Using GA-SVR
by Gaoliang Zhang, Zihan Ma, Chao Yan, Tianyan You and Jinhui Zhao
Foods 2026, 15(1), 134; https://doi.org/10.3390/foods15010134 - 2 Jan 2026
Viewed by 501
Abstract
The extensive use of sulfathiazole in poultry farming has raised growing concerns regarding its residues in poultry-derived products, posing risks to human health and food safety. To overcome the limitations of conventional detection methods and address the analytical challenges posed by inherent complexity [...] Read more.
The extensive use of sulfathiazole in poultry farming has raised growing concerns regarding its residues in poultry-derived products, posing risks to human health and food safety. To overcome the limitations of conventional detection methods and address the analytical challenges posed by inherent complexity of chicken blood matrix for the detection of sulfathiazole residues in chicken blood, a rapid and sensitive surface-enhanced Raman spectroscopy (SERS) method was developed for detecting sulfathiazole residues in chicken blood. Four colloidal substrates, i.e., gold colloid A, gold colloid B, gold colloid C, and silver colloids, were synthesized and evaluated for their SERS enhancement capabilities. Key parameters, including electrolyte type (NaCl solution), colloidal substrate type (gold colloid A), volume of gold colloid A (550 μL), volume of NaCl solution (60 μL), and adsorption time (14 min), were systematically optimized to maximize SERS intensities at 1157 cm−1. Furthermore, a genetic algorithm-support vector regression (GA-SVR) model integrated with adaptive iteratively reweighted penalized least squares (air-PLS) and multiplicative scatter correction (MSC) preprocessing demonstrated superior predictive performance with a prediction set coefficient of determination (R2p) value of 0.9278 and a root mean square error of prediction (RMSEP) of 3.1552. The proposed method demonstrated high specificity, minimal matrix interference, and robustness, making it suitable for reliable detection of sulfathiazole residues in chicken blood and compliant with global food safety requirements. Full article
(This article belongs to the Special Issue Chemometrics in Food Authenticity and Quality Control)
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11 pages, 9978 KB  
Article
Beluga Optimization Algorithm for Near-Infrared Spectral Variable Selection of Complex Samples
by Javaria Kousar, Liping Yang, Jiale Xiang, Qingwei Mao and Xihui Bian
Foods 2025, 14(24), 4266; https://doi.org/10.3390/foods14244266 - 11 Dec 2025
Cited by 2 | Viewed by 575
Abstract
Near-infrared (NIR) spectroscopy combined with multivariate calibration methods is widely used for the quantitative analysis of complex samples. However, the high-dimensional redundancy of spectra may compromise model predictive accuracy, making it necessary to select variables before modeling. The beluga whale optimization (BWO) algorithm [...] Read more.
Near-infrared (NIR) spectroscopy combined with multivariate calibration methods is widely used for the quantitative analysis of complex samples. However, the high-dimensional redundancy of spectra may compromise model predictive accuracy, making it necessary to select variables before modeling. The beluga whale optimization (BWO) algorithm is known for its fast convergence speed, high accuracy and few parameters. The present study employed the discretized BWO (DBWO) algorithm in conjunction with partial least squares (PLS) for spectral quantitative analysis of complex samples. After the optimal number of iterations and transfer function were determined, the PLS models were established based on the randomization test (RT), uninformative variable elimination (UVE) and Monte Carlo uninformative variable elimination (MC-UVE). The predictive performance of DBWO-PLS was compared with full-spectrum PLS, RT-PLS, UVE-PLS and MC-UVE-PLS using wheat, tablet and cocoa bean samples. The results show that all four variable selection methods enhanced model prediction accuracy, with the DBWO-PLS model notably achieving superior performance. Full article
(This article belongs to the Special Issue Chemometrics in Food Authenticity and Quality Control)
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