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Chemometrics in Food Chemistry and Analysis: Novel Detection Methods to...
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Chemometrics in Food Chemistry and Analysis: Novel Detection Methods to Assess Food Quality and Safety

A special issue of Foods (ISSN 2304-8158). This special issue belongs to the section "Food Analytical Methods".

Deadline for manuscript submissions: 30 July 2025 | Viewed by 333

Special Issue Editors

Dr. Elias Bou-Maroun

E-Mail Website1 Website2
Guest Editor
Institut Agro Dijon, University Burgundy Franche-Comté, INRAE, PAM UMR 1517, Food and Wine Science & Technology, F-21000 Dijon, France
Interests: molecularly imprinted polymers; analytical chemistry; food chemistry; sample preparation; chromatography; trace analysis
Special Issues, Collections and Topics in MDPI journals
Dr. Laurence Dujourdy

E-Mail Website
Guest Editor
Institut Agro Dijon, University Burgundy Franche-Comté, INRAE, PAM UMR 1517, Food and Wine Science & Technology, F-21000 Dijon, France
Interests: chromatography; mass spectrometry; drug analysis; chemicals instrumental analysis; validation method; validation sample; preparation analytical chemistry; instrumentation; statistics

Special Issue Information

Dear Colleagues,

The global food industry faces increasing challenges to ensure the quality, authenticity, and safety of food products. Chemometrics, as a powerful tool for extracting meaningful information from complex data sets, is revolutionizing the field of food chemistry and analysis. This Special Issue invites cutting-edge research and innovative methodologies that integrate chemometric approaches with advanced detection techniques. Topics of interest include, but are not limited to, multivariate analysis, machine learning applications, spectral data interpretation, and real-time monitoring solutions. Contributions that demonstrate the potential of chemometrics in enhancing food safety standards and improving the efficiency of quality control processes are particularly encouraged. Through this Special Issue, we aim to highlight the role of data-driven strategies in advancing food science and ensuring consumer protection in an increasingly complex global market.

Dr. Elias Bou-Maroun
Dr. Laurence Dujourdy
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

  • chemometrics
  • food analysis
  • quality control
  • food safety
  • detection methods
  • spectroscopy
  • multivariate analysis
  • machine learning
  • authenticity assessment
  • real-time monitoring

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

Published Papers (1 paper)

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Research

12 pages, 1621 KiB  
Article
The Effect of the Level of Goat Liver Addition to Goat Minced Meat on the Near-Infrared Spectra, Colour, and Shelf Life of Samples
by Louwrens Christiaan Hoffman, Wencong Wu, Shuxin Zhang, Michel Beya and Daniel Cozzolino
Foods 2025, 14(8), 1430; https://doi.org/10.3390/foods14081430 - 21 Apr 2025
Viewed by 186
Abstract
This study aimed to evaluate the utilisation of near-infrared (NIR) spectroscopy combined with chemometric techniques to identify the addition of goat liver to goat minced meat and to monitor the shelf life of the samples up to 8 days of storage. Mix samples [...] Read more.
This study aimed to evaluate the utilisation of near-infrared (NIR) spectroscopy combined with chemometric techniques to identify the addition of goat liver to goat minced meat and to monitor the shelf life of the samples up to 8 days of storage. Mix samples were created by adding goat liver to goat meat in different ratios (0%, 2%, 4%, 6%, and 8% w/w), and after mincing, the samples were stored under chilled (2–4 °C) conditions for 8 days. The NIR spectra, CIELab parameters, and pH of the mixture samples were collected at the start of the study and after 2, 4, 6, and 8 days of storage. The mince became darker with the increase in days of storage, while the pH value was not affected by days of storage. Partial least squares (PLS) regression was used to develop calibration models for the CIELab parameters to predict the level of liver addition to minced meat and to predict days of storage. The standard error in cross-validation (SECV) and the coefficient of determination in cross-validation (R2cv) were 0.10 (SECV: 3.3), 0.63 (SECV: 1.5), and 0.60 (SECV: 0.90) for L*, a*, and b*, respectively. The R2CV and SECV were 0.32 (SECV: 2.4%) and 0.92 (SECV: 0.98 days) to predict the level of liver addition to minced meat and days of storage, respectively. The NIR calibration models developed to predict the CIELab parameters and level of addition of liver to minced meat were inadequate for predicting new samples. On the other hand, the PLS models developed could predict the days of storage, R2cv 0.92 (SECV: 0.98 days). Compared with traditional methods such as CIELab or pH measurements, NIR spectroscopy can yield results more rapidly. However, the variability in the data set should be increased to allow the development of more reliable models. Full article
(This article belongs to the Special Issue Chemometrics in Food Chemistry and Analysis: Novel Detection Methods to Assess Food Quality and Safety)
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