Advances in Food Analysis: The Role of Chemometrics and Smart Analytical Systems

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

Deadline for manuscript submissions: closed (29 October 2025) | Viewed by 2956

Editors


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Guest Editor
Department of Chemistry, Sapienza University of Rome, Rome, Italy
Interests: chemometrics; NIR; thermogravimetry; GC-MS; food control

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Guest Editor
Department of Chemistry, “Sapienza” University of Rome, P.le Aldo Moro 5, 00185 Rome, Italy
Interests: chemometrics; forensic analysis; microNIR spectroscopy; thermal analysis
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Special Issue Information

Dear Colleagues,

Food products’ quality and safety continue to be the main concerns of manufacturers and suppliers involved in the food chain and authorities responsible for food control activities. Currently, scientific research is focused on the development of novel analytical tools able to perform a complete characterization of foodstuff and provide quality identifiers. Special attention is paid to smart screening systems, which allow us to conduct time-saving investigations throughout the food chain and effective on-site controls to prevent frauds. Chemometric techniques are considered necessary for the development of qualitative and quantitative prediction tools starting from targeted and/or non-targeted analysis. Multivariate statistical techniques allow us to process large amounts of data, extract the relevant chemical information, and validate robust classification or regression models monitoring nutritional, functional, and sensorial features of raw materials, intermediates, and final products.

In a context where rapid and accurate analytical strategies are increasingly required, this Special Issue aims to collect original research articles and reviews on the latest advances in food science and analysis, specifically in the authentication, traceability, and security of food products through chemometrics and smart screening systems.

Dr. Giuseppina Gullifa
Dr. Roberta Risoluti
Dr. Stefano Materazzi
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-anonymized 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 analysis
  • food quality control
  • food product characterization
  • food product authentication
  • chemometrics
  • multiparametric quality control
  • multivariate statistical analysis
  • smart screening systems
  • quality identifiers

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Published Papers (1 paper)

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Research

15 pages, 1352 KB  
Article
Authenticity Identification and Quantitative Analysis of Dendrobium officinale Based on Near-Infrared Spectroscopy Combined with Chemometrics
by Zhi-Liang Fan, Qian Li, Zhi-Tong Zhang, Lei Bai, Xiang Pu, Ting-Wei Shi and Yi-Hui Chai
Foods 2026, 15(1), 121; https://doi.org/10.3390/foods15010121 - 1 Jan 2026
Cited by 5 | Viewed by 1091
Abstract
Dendrobium officinale is a valuable medicinal and edible homologous health food. It has immunomodulatory, antioxidant, and metabolism-regulating properties. However, its adulteration is widespread, seriously compromising product quality and safety. Traditional adulteration detection methods are complex, costly, and time-consuming, making it urgent to establish [...] Read more.
Dendrobium officinale is a valuable medicinal and edible homologous health food. It has immunomodulatory, antioxidant, and metabolism-regulating properties. However, its adulteration is widespread, seriously compromising product quality and safety. Traditional adulteration detection methods are complex, costly, and time-consuming, making it urgent to establish a rapid and non-destructive detection approach. This study developed a rapid identification and quantification method for adulterated D. officinale. The method combined near-infrared (NIR) spectroscopy with data-driven soft independent modeling of class analogy (DD-SIMCA) and partial least squares regression (PLSR) models. PCA, PLS-DA, and OPLS-DA were first used to visualize sample clustering and group differences. DT, SVM, ANN, and NB were used for classification. DD-SIMCA and PLSR were used for one-class modeling and quantitative analysis. Raw spectral data were preprocessed using multiplicative scatter correction (MSC), the standard normal variate (SNV), the first derivative, and Savitzky–Golay smoothing. In the identification analysis, the DD-SIMCA model achieved 100% sensitivity and 100% specificity in the validation set. Its overall accuracy in the independent test set was 99.2%, demonstrating excellent discrimination performance. In addition, SVM combined with NIR also achieved good accuracy. In the quantitative analysis of adulteration, the PLSR model predicted different adulteration levels. Most calibration and validation sets showed R2 values above 0.99 and RMSE values below 0.05, indicating excellent predictive performance. The results indicate that NIR combined with DD-SIMCA and PLSR can achieve rapid identification and accurate quantification of adulterated D. officinale samples. This approach provides strong support for quality control and regulatory supervision of high-value health foods. Full article
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