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: 31 March 2025 | Viewed by 2087

Special Issue Editor


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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
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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

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Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2700 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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Published Papers (1 paper)

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Research

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
Viewed by 663
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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