Advances in Chemometrics in Analytical Chemistry

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Chemical and Molecular Sciences".

Deadline for manuscript submissions: closed (29 February 2024) | Viewed by 2642

Special Issue Editors


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Guest Editor
School of Chemistry, Sun Yat-Sen University, Guangzhou 510275, China
Interests: chemometrics; analytical chemistry

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Guest Editor
Department of Public Health Sciences, University of Rochester School of Medicine and Dentistry, Saunders Research Building 3.304, 265 Crittenden Boulevard, CU 420644, Rochester, NY 14642, USA
Interests: chemometrics; fluorescent probe

Special Issue Information

Dear Colleagues,

Chemometrics is a highly interdisciplinary field closely related to analytical chemistry. It forms part of the basis of chemistry, mainly from the point of view of chemical composition and chemical structure, as well as studies to measure the nature of chemical substances.

This Special Issue aims to explore in detail the application of chemometrics to analytical chemistry, with the aim of promoting the development of chemometrics. Chemometrics includes all the processes of chemical measurement. In the continuous development of chemistry, the main role of chemometrics is reflected in the analysis, design, and processing of chemical measurement data. In chemical research, it is necessary to involve chemometrics in chemical measurement. Since the introduction of new analytical methods based on chemical instruments to analytical chemistry in the 1950s, analysis and testing work has gradually realized instrumentation, automation, and preliminary computerization. The in-depth application of these technologies provides reliable measurement data for chemical analysis, and the advantages of these analytical instruments can be combined to carry out heavy data analysis in the context of information chaos. The development of new arrangements and combinations to solve the maximum information screening has become the biggest problem facing chemical researchers.

This Special Issue focuses on all aspects of the application of stoichiometry to analytical chemistry, including experimental design, instrumental data analysis and processing, and new applications of known methods in the context of novel techniques and algorithms.

Prof. Dr. Feng Gan
Prof. Dr. Philip K. Hopke
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. Applied Sciences 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 2400 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

  • analytical chemistry
  • chemometrics
  • principal component analysis
  • classification
  • characterization

Published Papers (2 papers)

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Research

13 pages, 3605 KiB  
Article
Two Revised Deep Neural Networks and Their Applications in Quantitative Analysis Based on Near-Infrared Spectroscopy
by Hong-Hua Huang, Jian-Fei Luo, Feng Gan and Philip K. Hopke
Appl. Sci. 2023, 13(14), 8494; https://doi.org/10.3390/app13148494 - 23 Jul 2023
Viewed by 872
Abstract
Small data sets make developing calibration models using deep neural networks difficult because it is easy to overfit the system. We developed two deep neural network architectures by revising two existing network architectures: the U-Net and the attention mechanism. The major changes were [...] Read more.
Small data sets make developing calibration models using deep neural networks difficult because it is easy to overfit the system. We developed two deep neural network architectures by revising two existing network architectures: the U-Net and the attention mechanism. The major changes were to use 1D convolutional layers to replace the fully connected layers. We also designed and combined average pooling and maximum pooling in our revised networks, respectively. We applied these revised network architectures to three publicly available data sets and the resulting calibration models can generate acceptable results for general quantitative analysis. It also generated rather good results for data sets that concern calibration transfer. It demonstrates that constructing network architectures by properly revising existing successful network architectures may provide additional choices in the exploration of the application of deep neural network in analytical chemistry. Full article
(This article belongs to the Special Issue Advances in Chemometrics in Analytical Chemistry)
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11 pages, 1481 KiB  
Article
Characterizing Variances of Adulterated Extra Virgin Olive Oils by UV-Vis Spectroscopy Combined with Analysis of Variance-Projected Difference Resolution (ANOVA-PDR) and Multivariate Classification
by Boyan Gao, Jingyao Zhang and Weiying Lu
Appl. Sci. 2023, 13(7), 4360; https://doi.org/10.3390/app13074360 - 29 Mar 2023
Cited by 4 | Viewed by 1058
Abstract
The analysis of variance-projected difference resolution (ANOVA-PDR) was proposed and compared with multivariate classification for its potential in detecting possible food adulteration in extra virgin olive oils (EVOOs) by UV-Vis spectra. Three factors including origin, adulteration level, and adulteration type were systematically examined [...] Read more.
The analysis of variance-projected difference resolution (ANOVA-PDR) was proposed and compared with multivariate classification for its potential in detecting possible food adulteration in extra virgin olive oils (EVOOs) by UV-Vis spectra. Three factors including origin, adulteration level, and adulteration type were systematically examined by the ANOVA-derived methods. The ANOVA-PDR quantitatively presented the separation of the internal classes according to the three main factors. Specifically, the average ANOVA-derived PDRs of the EVOO origination and adulteration level, respectively, is 4.01 and 1.78, while the conventional PDRs of the three factors are all less than 1.5. Furthermore, the partial least-squares-discriminant analysis (PLS-DA) and the PLS regression (PLSR) modeling with the selected sub-datasets from different origins were used to verify the results. The resulting models suggested that the three main factors and their interactions were all important sources of spectral variations. Full article
(This article belongs to the Special Issue Advances in Chemometrics in Analytical Chemistry)
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