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Molecules
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Chemometrics Tools in Analytical Chemistry 2.0
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Chemometrics Tools in Analytical Chemistry 2.0

A special issue of Molecules (ISSN 1420-3049). This special issue belongs to the section "Analytical Chemistry".

Deadline for manuscript submissions: 31 August 2024 | Viewed by 2321

Special Issue Editors

Prof. Dr. Karoly Heberger

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Guest Editor
Research Centre for Natural Sciences, Hungarian Academy of Sciences, Budapest, Hungary
Interests: chemometrics; pattern recognition; ranking; chromatography; method comparison; validation; performance parameters
Prof. Dr. Thomas Bocklitz

E-Mail Website
Guest Editor
1. Leibniz Institute of Photonic Technology, Member of Leibniz Health Technologies, Member of the Leibniz Centre for Photonics in Infection Research (LPI), Jena, Germany
2. Institute of Physical Chemistry (IPC) and Abbe Center of Photonics (ACP), Friedrich Schiller University Jena, Member of the Leibniz Centre for Photonics in Infection Research (LPI), Jena, Germany
3. Institute of Computer Science, Faculty of Mathematics, Physics & Computer Science, University Bayreuth, Bayreuth, Germany
Interests: chemometrics; machine learning; data fusion; biomedical imaging and spectroscopy

Special Issue Information

Dear Colleagues,

The development of new instruments and hyphenated techniques as well as new analytical strategies such as profiling and fingerprinting contribute to obtaining a large amount of data characterizing the systems studied. These methodologies require the use of chemometric tools for data analysis. In modern analytical chemistry, chemometric methods are used to design experiments and to extract analytical information from the multivariate and multiway data acquired during experiments. Unsupervised methods are used for data visualization and exploration. Supervised methods are applied for classification and calibration. In research, chemometric methods enable the modeling properties of chemical systems and discover the structure and relationships of the data. Machine learning models have revolutionized calibration, classification, etc. Their validations are dubious or at least not accepted generally. The multivariate models developed using chemometric methods are the basis for the practical application of instrumental techniques in many fields including food analysis, process analytical technology, environmental control, medical, pharmaceutical, biological, and forensic fields.

This Special Issue aims to cover original research papers and reviews related to the development of new multivariate and multiway methods and to methodological aspects of chemometric research, such as model optimization, preprocessing, variable selection, and data fusion. Application-oriented papers related to using chemometrics in different fields are also very welcome.

Prof. Dr. Karoly Heberger
Prof. Dr. Thomas Bocklitz
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. Molecules 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 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

  • design of experiments (DoE)
  • multivariate methods
  • multiway methods
  • exploratory data analysis
  • classification and calibration
  • model optimization
  • pattern recognition
  • application of chemometrics

Related Special Issue

Published Papers (3 papers)

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Research

13 pages, 1342 KiB  
Article
Approximated Uncertainty Propagation of Correlated Independent Variables Using the Ordinary Least Squares Estimator
by Jeong Sik Lim, Yong Doo Kim and Jin-Chun Woo
Molecules 2024, 29(6), 1248; https://doi.org/10.3390/molecules29061248 - 11 Mar 2024
Viewed by 567
Abstract
For chemical measurements, calibration is typically conducted by regression analysis. In many cases, generalized approaches are required to account for a complex-structured variance–covariance matrix of (in)dependent variables. However, in the particular case of highly correlated independent variables, the ordinary least squares (OLS) method [...] Read more.
For chemical measurements, calibration is typically conducted by regression analysis. In many cases, generalized approaches are required to account for a complex-structured variance–covariance matrix of (in)dependent variables. However, in the particular case of highly correlated independent variables, the ordinary least squares (OLS) method can play a rational role with an approximated propagation of uncertainties of the correlated independent variables into that of a calibrated value for a particular case in which standard deviation of fit residuals are close to the uncertainties along the ordinate of calibration data. This proposed method aids in bypassing an iterative solver for the minimization of the implicit form of the squared residuals. This further allows us to derive the explicit expression of budgeted uncertainties corresponding to a regression uncertainty, the measurement uncertainty of the calibration target, and correlated independent variables. Explicit analytical expressions for the calibrated value and associated uncertainties are given for straight-line and second-order polynomial fit models for the highly correlated independent variables. Full article
(This article belongs to the Special Issue Chemometrics Tools in Analytical Chemistry 2.0)
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16 pages, 3440 KiB  
Article
Siamese Networks for Clinically Relevant Bacteria Classification Based on Raman Spectroscopy
by Jhonatan Contreras, Sara Mostafapour, Jürgen Popp and Thomas Bocklitz
Molecules 2024, 29(5), 1061; https://doi.org/10.3390/molecules29051061 - 28 Feb 2024
Viewed by 648
Abstract
Identifying bacterial strains is essential in microbiology for various practical applications, such as disease diagnosis and quality monitoring of food and water. Classical machine learning algorithms have been utilized to identify bacteria based on their Raman spectra. However, convolutional neural networks (CNNs) offer [...] Read more.
Identifying bacterial strains is essential in microbiology for various practical applications, such as disease diagnosis and quality monitoring of food and water. Classical machine learning algorithms have been utilized to identify bacteria based on their Raman spectra. However, convolutional neural networks (CNNs) offer higher classification accuracy, but they require extensive training sets and retraining of previous untrained class targets can be costly and time-consuming. Siamese networks have emerged as a promising solution. They are composed of two CNNs with the same structure and a final network that acts as a distance metric, converting the classification problem into a similarity problem. Classical machine learning approaches, shallow and deep CNNs, and two Siamese network variants were tailored and tested on Raman spectral datasets of bacteria. The methods were evaluated based on mean sensitivity, training time, prediction time, and the number of parameters. In this comparison, Siamese-model2 achieved the highest mean sensitivity of 83.61 ± 4.73 and demonstrated remarkable performance in handling unbalanced and limited data scenarios, achieving a prediction accuracy of 73%. Therefore, the choice of model depends on the specific trade-off between accuracy, (prediction/training) time, and resources for the particular application. Classical machine learning models and shallow CNN models may be more suitable if time and computational resources are a concern. Siamese networks are a good choice for small datasets and CNN for extensive data. Full article
(This article belongs to the Special Issue Chemometrics Tools in Analytical Chemistry 2.0)
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10 pages, 1649 KiB  
Communication
Lipase-Assisted Synthesis of Alkyl Stearates: Optimization by Taguchi Design of Experiments and Application as Defoamers
by Enoch Olvera-Ureña, Jorge Lopez-Tellez, M. Monserrat Vizueto, J. Guadalupe Hidalgo-Ledezma, Baltazar Martinez-Quiroz and Jose A. Rodriguez
Molecules 2024, 29(1), 195; https://doi.org/10.3390/molecules29010195 - 29 Dec 2023
Viewed by 719
Abstract
The present work proposes the optimization of enzymatic synthesis of alkyl stearates using stearic acid, alkyl alcohols (C1-OH, C2-OH, C4-OH, C8-OH and C16-OH) and Candida rugosa lipase by a L9 (34 [...] Read more.
The present work proposes the optimization of enzymatic synthesis of alkyl stearates using stearic acid, alkyl alcohols (C1-OH, C2-OH, C4-OH, C8-OH and C16-OH) and Candida rugosa lipase by a L9 (34) Taguchi-type design of experiments. Four variables were evaluated (reaction time, temperature, kU of lipase and alcohol:stearic acid molar ratio), ensuring that all variables were critical. In optimal conditions, five stearates were obtained with conversions > 90%. The obtained products were characterized by nuclear magnetic resonance (NMR). Additionally, the defoaming capacity of the five stearates was evaluated, obtaining better performance for the compound synthesized from C8-OH alcohol. Full article
(This article belongs to the Special Issue Chemometrics Tools in Analytical Chemistry 2.0)
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