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Chemosensors
Special Issues
Chemometrics for Food, Environmental and Biological Analysis
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Chemometrics for Food, Environmental and Biological Analysis

A special issue of Chemosensors (ISSN 2227-9040). This special issue belongs to the section "Analytical Methods, Instrumentation and Miniaturization".

Deadline for manuscript submissions: 30 September 2025 | Viewed by 3633

Special Issue Editor

Prof. Dr. Aderval Severino Luna

E-Mail Website
Guest Editor
Analytical Chemistry Department, Rio de Janeiro State University, Rio de Janeiro 20559-900, Brazil
Interests: chemometrics; modeling of instrumental signals; machine learning; classification and regression tasks; chemical fingerprints; atomic and molecular spectroscopy; food analysis
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Chemometrics involves the application of mathematical and statistical methods to analyze chemical data. Several major topics are associated with chemometrics in the context of food, environmental, and biological analysis. These include the following:

  • Multivariate data analysis: techniques such as principal component analysis (PCA), partial least squares (PLS), and cluster analysis simultaneously handle data with multiple variables. They help in identifying patterns, correlations, and groupings within complex datasets.
  • Spectral Analysis: methods like fourier-transform infrared (FTIR) spectroscopy, nuclear magnetic resonance (NMR), and mass spectrometry are employed to analyze spectral data from various samples in food, environmental, and biological analysis. Chemometrics helps interpret and extract meaningful information from these spectra.
  • Chemical Fingerprinting: This involves using chemometric methods to create characteristic patterns or fingerprints of samples based on their chemical composition. It is instrumental in identifying and authenticating food products, environmental pollutants, or biological substances.
  • Pattern Recognition: Chemometrics facilitates the recognition of complex patterns within datasets, aiding in classifying and discriminating between samples based on specific characteristics or properties.
  • Quality Control and Assurance: Chemometrics is crucial in assessing the quality, authenticity, and safety of food products, environmental samples, and biological materials. It helps in monitoring and controlling factors that affect these aspects.
  • Calibration and Predictive Modeling: Instrumental analysis uses chemometrics for calibration purposes to relate the measurements obtained from instruments to the properties of interest. It enables the creation of predictive models for estimating unknown properties of samples.
  • Experimental Design: Chemometrics assists in designing experiments effectively by optimizing the number and arrangement of experimental runs. It aids in maximizing the information gained while minimizing the number of experiments required.
  • Data Preprocessing and Feature Selection: Techniques like data normalization, outlier detection, and variable selection are vital in preparing data for analysis. Chemometrics methods help preprocess data to improve the quality and relevance of results.
  • Quantitative Analysis: Chemometrics enables the quantification of various components or analytes present in food, environmental, or biological samples, even in complex matrices, by utilizing calibration models and regression techniques.
  • Environmental Monitoring and Pollution Analysis: Chemometrics is widely applied in monitoring environmental pollutants, assessing their impact, identifying sources of contamination, and aiding in environmental management and remediation strategies.

These topics illustrate the broad applications of chemometrics in analyzing and interpreting data from diverse sources in food, environmental, and biological analysis. Therefore, research papers related to the topics above will be accepted in the Special Issue, titled “Chemosensors: Chemometrics for Food, Environmental, and Biological Analysis”.

Prof. Dr. Aderval Severino Luna
Guest Editor

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. Chemosensors is an international peer-reviewed open access monthly 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 2000 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

  • multivariate data analysis
  • spectral analysis
  • chemical fingerprint
  • pattern recognition
  • quality control and assurance
  • calibration and predictive modeling
  • experimental design
  • data processing and feature selection
  • quantitative analysis
  • environmental monitoring
  • pollution analysis

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

Published Papers (3 papers)

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Research

21 pages, 5044 KiB  
Article
¹H-NMR Spectroscopy and Chemometric Fingerprinting for the Authentication of Organic Extra Virgin Olive Oils
by Silvana M. Azcarate, Maria P. Segura-Borrego, Rocío Ríos-Reina and Raquel M. Callejón
Chemosensors 2025, 13(5), 162; https://doi.org/10.3390/chemosensors13050162 - 1 May 2025
Viewed by 191
Abstract
The authentication of organic extra virgin olive oils (OEVOOs) is crucial for quality control and fraud prevention. This study applies proton-nuclear magnetic resonance (1H-NMR) spectroscopy combined with chemometric analysis as a non-destructive, untargeted approach to differentiate EVOOs based on cultivation method [...] Read more.
The authentication of organic extra virgin olive oils (OEVOOs) is crucial for quality control and fraud prevention. This study applies proton-nuclear magnetic resonance (1H-NMR) spectroscopy combined with chemometric analysis as a non-destructive, untargeted approach to differentiate EVOOs based on cultivation method (organic vs. conventional) and variety (Hojiblanca vs. Picual). Principal component analysis (PCA) and partial least squares-discriminant analysis (PLS-DA) demonstrated well-defined sample differentiation, while the variable importance in projection (VIP) selection and Tukey’s test identified key spectral regions responsible for classification. The results showed that sterols and lipid-related compounds played a major role in distinguishing organic from conventional oils, whereas fatty acids and phenolic compounds were more relevant for cultivar differentiation. These findings align with known metabolic differences, where Picual oils generally exhibit higher polyphenol content, and a distinct fatty acid composition compared to Hojiblanca. The agreement between chemometric classification models and statistical tests supports the potential of 1H-NMR for OEVOO authentication. This method provides a comprehensive and reproducible metabolic fingerprint, enabling differentiation based on both agronomic practices and genetic factors. These findings suggest that 1H-NMR spectroscopy, coupled with multivariate analysis, could be a valuable tool for quality control and fraud detection in the olive oil industry. Full article
(This article belongs to the Special Issue Chemometrics for Food, Environmental and Biological Analysis)
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17 pages, 850 KiB  
Article
Validation of an LC-MS Method for Quantification of Mycotoxins and Characterization of Fungal Strains Occurring in Food and Feed
by Julien Masquelier, Emmanuel K. Tangni, Pierre Becker, Julie Sanders, Joëlle Laporte and Birgit Mertens
Chemosensors 2025, 13(3), 106; https://doi.org/10.3390/chemosensors13030106 - 14 Mar 2025
Viewed by 702
Abstract
Mycotoxins are naturally occurring secondary metabolites produced by specific fungal strains. They can cause adverse effects, posing a serious health threat to both humans and livestock. Focusing on several mycotoxins, this study first aimed at optimizing and validating an ultra-high liquid chromatography-tandem mass [...] Read more.
Mycotoxins are naturally occurring secondary metabolites produced by specific fungal strains. They can cause adverse effects, posing a serious health threat to both humans and livestock. Focusing on several mycotoxins, this study first aimed at optimizing and validating an ultra-high liquid chromatography-tandem mass spectrometry quantification method. This method was then applied to evaluate the production of the targeted mycotoxins in maize cultivated in the presence of Aspergillus spp., Fusarium spp., and Alternaria spp. The limits of detection of the analytical method for the different mycotoxins ranged between 0.5 and 200 μg kg−1, while the limits of quantification were between 1 and 400 μg kg−1. The linearities of the calibration curves were evaluated, with calculated R2 values above 0.99. The mean recoveries fell within the acceptable range of 74.0–106.0%, the repeatability was not higher than 14.4% RSD, and the highest intra-laboratory reproducibility was 16.2% RSD. The expanded measurement uncertainties ranged between 4.0% and 54.7%. Several fungal strains cultivated on maize grains were demonstrated to produce the targeted toxins, with production at µg kg−1 to mg kg−1 levels for aflatoxins and up to g kg−1 levels for fumonisins, zearalenone, and alternariol. Full article
(This article belongs to the Special Issue Chemometrics for Food, Environmental and Biological Analysis)
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13 pages, 2802 KiB  
Article
Profiling of Australian Stingless Bee Honey Using Multivariate Data Analysis of High-Performance Thin-Layer Chromatography Fingerprints
by Mariana Mello dos Santos, Christina Jacobs, Kevin Vinsen, Md Khairul Islam, Tomislav Sostaric, Lee Yong Lim and Cornelia Locher
Chemosensors 2025, 13(2), 30; https://doi.org/10.3390/chemosensors13020030 - 22 Jan 2025
Cited by 1 | Viewed by 1100
Abstract
The complex chemical composition of honey presents significant challenges for its analysis with variations influenced by factors such as botanical source, geographical location, bee species, harvest time, and storage conditions. This study aimed to employ high-performance thin-layer chromatography (HPTLC) fingerprinting, coupled with multivariate [...] Read more.
The complex chemical composition of honey presents significant challenges for its analysis with variations influenced by factors such as botanical source, geographical location, bee species, harvest time, and storage conditions. This study aimed to employ high-performance thin-layer chromatography (HPTLC) fingerprinting, coupled with multivariate data analysis, to characterise the chemical profiles of Australian stingless bee honey samples from two distinct bee species, Tetragonula carbonaria and Tetragonula hockingsi. Using a mobile phase composed of toluene:ethyl acetate:formic acid (6:5:1) and two derivatisation reagents, vanillin–sulfuric acid and natural product reagent/PEG, HPTLC fingerprints were developed to reveal characteristic patterns within the samples. Multivariate data analysis was employed to explore the similarities in the fingerprints and identify underlying patterns. The results demonstrated that the chemical profiles were more closely related to harvest time rather than bee species, as samples collected within the same month clustered together. The quality of the clustering results was assessed using silhouette scores. The study highlights the value of combining HPTLC fingerprinting with multivariate data analysis to produce valuable data that can aid in blending strategies and the creation of reference standards for future quality control analyses. Full article
(This article belongs to the Special Issue Chemometrics for Food, Environmental and Biological Analysis)
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