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Chemosensors
Special Issues
Chemometrics Tools Used in Chemical Detection and Analysis
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Chemometrics Tools Used in Chemical Detection and 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 2024 | Viewed by 2310

Special Issue Editor

Dr. Pedro N. Sousa Sampaio

E-Mail Website
Guest Editor
Computação e Cognição Centrada nas Pessoas (BioRG—Biomedical Research Group), Lusofona University, Campo Grande, 376, 1749-019 Lisbon, Portugal
Interests: chemometrics; machine learning; infrared spectroscopy; bioactive compounds
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Chemometrics is defined as the application of statistical and mathematical methods to analytical data to permit the maximum collection and extraction of useful information. According to the area of study, chemometrics can be divided into several areas, namely, the processing of analytical signals, the planning and optimization of experiments, pattern recognition and data classification, multivariate calibration, artificial intelligence methods, among others. This tool allows for the better extraction and knowledge of chemical or biochemical systems by obtaining specific information that would otherwise be more complex and time consuming or wrongly interpreted. The utility of chemometric techniques as tools enabling the multidimensional calibration of selected spectroscopic, electrochemical, and chromatographic methods is demonstrated. The uses of this approach, mainly for the interpretation of UV–Vis, near-IR (NIR), or mid-IR (MIR) spectra, as well as for data obtained with other instrumental methods, make identification and the quantitative analysis of active substances in complex mixtures possible. This special Issue aims to share knowledge and experiences in relation to the use and exploration of different and multifaceted chemometric techniques in areas such as chemistry, biochemistry, pharmaceuticals, food, beverages, etc. I, therefore, wish to invite all those interested in publishing their research work or reviews in this Special Issue addressing the most diverse areas of chemometrics.

Dr. Pedro N. Sousa Sampaio
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 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

  • chemometrics
  • machine learning
  • data mining
  • multivariate data analysis
  • optimization

Published Papers (1 paper)

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Research

15 pages, 1282 KiB  
Article
Spectroscopy and Chemometrics for Conformity Analysis of e-Liquids: Illegal Additive Detection and Nicotine Characterization
by Zeb Akhtar, Sophia Barhdadi, Kris De Braekeleer, Cedric Delporte, Erwin Adams and Eric Deconinck
Chemosensors 2024, 12(1), 9; https://0-doi-org.brum.beds.ac.uk/10.3390/chemosensors12010009 - 05 Jan 2024
Cited by 1 | Viewed by 1713
Abstract
Vaping electronic cigarettes (e-cigarettes) has become a popular alternative to smoking tobacco. When an e-cigarette is activated, a liquid is vaporized by heating, producing an aerosol that users inhale. While e-cigarettes are marketed as less harmful than traditional cigarettes, there are ongoing concerns [...] Read more.
Vaping electronic cigarettes (e-cigarettes) has become a popular alternative to smoking tobacco. When an e-cigarette is activated, a liquid is vaporized by heating, producing an aerosol that users inhale. While e-cigarettes are marketed as less harmful than traditional cigarettes, there are ongoing concerns about their long-term health effects, including potential lung damage. Therefore, it is essential to closely monitor and study the composition of e-liquids. E-liquids typically consist of propylene glycol, glycerin, flavorings and nicotine, though there have been reports of non-compliant nicotine concentrations and the presence of illegal additives. This study explored spectroscopic techniques to examine the conformity of nicotine labeling and detect the presence of the not-allowed additives: the caffeine, taurine, vitamin E and cannabidiol (CBD) in e-liquids. A total of 236 e-liquid samples were carefully selected for analysis. Chemometric analysis was applied to the collected data, which included mid-infrared (MIR) and near-infrared (NIR) spectra. Supervised modeling approaches such as partial least squares-discriminant analysis (PLS-DA) and soft independent modeling of class analogy (SIMCA) were employed to classify the samples, based on the presence of nicotine and the targeted additives. This study demonstrates the efficacy of MIR and NIR spectroscopic techniques in conjunction with chemometric methods (SIMCA and PLS-DA) for detecting specific molecules in e-liquids. MIR with autoscaling data preprocessing and PLS-DA achieved 100% classification rates for CBD and vitamin E, while NIR with the same approach achieved 100% for CBD and taurine. Overall, MIR combined with PLS-DA yielded the best classification across all targeted molecules, suggesting its preference as a singular technique. Full article
(This article belongs to the Special Issue Chemometrics Tools Used in Chemical Detection and Analysis)
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