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Molecules
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Development of Chemometrics: Now and Future
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Development of Chemometrics: Now and Future

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

Deadline for manuscript submissions: closed (28 February 2023) | Viewed by 6540

Special Issue Editors

Dr. Mirta Raquel Alcaraz

E-Mail Website
Guest Editor
Laboratorio de Desarrollo Analítico y Quimiometría (LADAQ), Cátedra de Química Analítica I, Facultad de Bioquímica y Ciencias Biológicas, Universidad Nacional del Litoral, Ciudad Universitaria, Santa Fe S3000ZAA, Argentina
Interests: chemometrics; multi-way data analysis; analytical method development; chromatography; spectroscopy; multivariate calibration
Dr. Héctor Casimiro Goicoechea

E-Mail Website
Guest Editor
Laboratorio de Desarrollo Analítico y Quimiometría (LADAQ), Cátedra de Química Analítica I, Facultad de Bioquímica y Ciencias Biológicas, Universidad Nacional del Litoral, Ciudad Universitaria, Santa Fe S3000ZAA, Argentina
Interests: chemometrics; multi-way data analysis; analytical method development; experimental design; multivariate calibration; classification

Special Issue Information

Dear Colleagues,

In recent decades, the use of chemometrics to enhance the performance of experimental work has gained the attention of the researcher community. This phenomenon is because chemometrics helps to obtain a large amount of information about the system, which is sometimes hindered in the data. 

It has been demonstrated that using chemometrics outperforms the advantages of classical data analysis and influences experimental work, such as reducing the time of work, decreasing solvent consumption, and avoiding sample pre-processing steps. 

Chemometrics is a versatile discipline in continuous growth, in light of providing new and efficient tools for the scientific community of different fields of interest. 

This Special Issue aims to present the developments of chemometric strategies that have taken place over the last decade, as well as their application for different purposes (calibration, classification, descriptive analysis, etc.) in several fields.

Because of this, research manuscripts where chemometric strategies are developed and implemented to solve complex systems in different areas of interest are very welcome.

Dr. Mirta Raquel Alcaraz
Dr. Héctor Casimiro Goicoechea
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

  • chemometrics
  • multi-way data analysis
  • classification
  • calibration
  • evolving analysis

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Published Papers (3 papers)

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Research

16 pages, 1306 KiB  
Article
Multidimensional Chromatographic Fingerprinting Combined with Chemometrics for the Identification of Regulated Plants in Suspicious Plant Food Supplements
by Surbhi Ranjan, Erwin Adams and Eric Deconinck
Molecules 2023, 28(8), 3632; https://doi.org/10.3390/molecules28083632 - 21 Apr 2023
Viewed by 1614
Abstract
The popularity of plant food supplements has seen explosive growth all over the world, making them susceptible to adulteration and fraud. This necessitates a screening approach for the detection of regulated plants in plant food supplements, which are usually composed of complex plant [...] Read more.
The popularity of plant food supplements has seen explosive growth all over the world, making them susceptible to adulteration and fraud. This necessitates a screening approach for the detection of regulated plants in plant food supplements, which are usually composed of complex plant mixtures, thus making the approach not so straightforward. This paper aims to tackle this problem by developing a multidimensional chromatographic fingerprinting method aided by chemometrics. To render more specificity to the chromatogram, a multidimensional fingerprint (absorbance × wavelength × retention time) was considered. This was achieved by selecting several wavelengths through a correlation analysis. The data were recorded using ultra-high-performance liquid chromatography (UHPLC) coupled with diode array detection (DAD). Chemometric modelling was performed by partial least squares–discriminant analysis (PLS-DA) through (a) binary modelling and (b) multiclass modelling. The correct classification rates (ccr%) by cross-validation, modelling, and external test set validation were satisfactory for both approaches, but upon further comparison, binary models were preferred. As a proof of concept, the models were applied to twelve samples for the detection of four regulated plants. Overall, it was revealed that the combination of multidimensional fingerprinting data with chemometrics was feasible for the identification of regulated plants in complex botanical matrices. Full article
(This article belongs to the Special Issue Development of Chemometrics: Now and Future)
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14 pages, 2136 KiB  
Article
Partial Least Squares, Experimental Design, and Near-Infrared Spectrophotometry for the Remote Quantification of Nitric Acid Concentration and Temperature
by Luke R. Sadergaski, Sawyer B. Irvine and Hunter B. Andrews
Molecules 2023, 28(7), 3224; https://doi.org/10.3390/molecules28073224 - 4 Apr 2023
Cited by 5 | Viewed by 2061
Abstract
Near-infrared spectrophotometry and partial least squares regression (PLSR) were evaluated to create a pleasantly simple yet effective approach for measuring HNO3 concentration with varying temperature levels. A training set, which covered HNO3 concentrations (0.1–8 M) and temperature (10–40 °C), was selected [...] Read more.
Near-infrared spectrophotometry and partial least squares regression (PLSR) were evaluated to create a pleasantly simple yet effective approach for measuring HNO3 concentration with varying temperature levels. A training set, which covered HNO3 concentrations (0.1–8 M) and temperature (10–40 °C), was selected using a D-optimal design to minimize the number of samples required in the calibration set for PLSR analysis. The top D-optimal-selected PLSR models had root mean squared error of prediction values of 1.4% for HNO3 and 4.0% for temperature. The PLSR models built from spectra collected on static samples were validated against flow tests including HNO3 concentration and temperature gradients to test abnormal conditions (e.g., bubbles) and the model performance between sample points in the factor space. Based on cross-validation and prediction modeling statistics, the designed near-infrared absorption approach can provide remote, quantitative analysis of HNO3 concentration and temperature for production-oriented applications in facilities where laser safety challenges would inhibit the implementation of other optical techniques (e.g., Raman spectroscopy) and in which space, time, and/or resources are constrained. The experimental design approach effectively minimized the number of samples in the training set and maintained or improved PLSR model performance, which makes the described chemometric approach more amenable to nuclear field applications. Full article
(This article belongs to the Special Issue Development of Chemometrics: Now and Future)
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14 pages, 9721 KiB  
Article
Performance Optimization of a Developed Near-Infrared Spectrometer Using Calibration Transfer with a Variety of Transfer Samples for Geographical Origin Identification of Coffee Beans
by Nutthatida Phuangsaijai, Parichat Theanjumpol and Sila Kittiwachana
Molecules 2022, 27(23), 8208; https://doi.org/10.3390/molecules27238208 - 25 Nov 2022
Cited by 7 | Viewed by 2282
Abstract
This research aimed to improve the classification performance of a developed near-infrared (NIR) spectrometer when applied to the geographical origin identification of coffee bean samples. The modification was based on the utilization of a collection of spectral databases from several different agricultural samples, [...] Read more.
This research aimed to improve the classification performance of a developed near-infrared (NIR) spectrometer when applied to the geographical origin identification of coffee bean samples. The modification was based on the utilization of a collection of spectral databases from several different agricultural samples, including corn, red beans, mung beans, black beans, soybeans, green and roasted coffee, adzuki beans, and paddy and white rice. These databases were established using a reference NIR instrument and the piecewise direct standardization (PDS) calibration transfer method. To evaluate the suitability of the transfer samples, the Davies–Bouldin index (DBI) was calculated. The outcomes that resulted in low DBI values were likely to produce better classification rates. The classification of coffee origins was based on the use of a supervised self-organizing map (SSOM). Without the spectral modification, SSOM classification using the developed NIR instrument resulted in predictive ability (% PA), model stability (% MS), and correctly classified instances (% CC) values of 61%, 58%, and 64%, respectively. After the transformation process was completed with the corn, red bean, mung bean, white rice, and green coffee NIR spectral data, the predictive performance of the SSOM models was found to have improved (67–79% CC). The best classification performance was observed with the use of corn, producing improved % PA, % MS, and % CC values at 71%, 67%, and 79%, respectively. Full article
(This article belongs to the Special Issue Development of Chemometrics: Now and Future)
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