Chemometrics for Selection, Prediction, and Classification of Sustainable Solutions for Green Chemistry—A Review
Department of Analytical Chemistry, Faculty of Chemistry, Gdańsk University of Technology (GUT), 80-233 Gdańsk, Poland
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Symmetry 2020, 12(12), 2055; https://doi.org/10.3390/sym12122055
Received: 13 November 2020
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Revised: 7 December 2020
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Accepted: 9 December 2020
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Published: 11 December 2020
(This article belongs to the Special Issue Chemometrics in Assessing Molecular Structures and Properties)
In this review, we present the applications of chemometric techniques for green and sustainable chemistry. The techniques, such as cluster analysis, principal component analysis, artificial neural networks, and multivariate ranking techniques, are applied for dealing with missing data, grouping or classification purposes, selection of green material, or processes. The areas of application are mainly finding sustainable solutions in terms of solvents, reagents, processes, or conditions of processes. Another important area is filling the data gaps in datasets to more fully characterize sustainable options. It is significant as many experiments are avoided, and the results are obtained with good approximation. Multivariate statistics are tools that support the application of quantitative structure–property relationships, a widely applied technique in green chemistry.
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Keywords:
multivariate statistics; sustainable chemistry; missing data; classification; grouping; solvents
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MDPI and ACS Style
Bystrzanowska, M.; Tobiszewski, M. Chemometrics for Selection, Prediction, and Classification of Sustainable Solutions for Green Chemistry—A Review. Symmetry 2020, 12, 2055. https://doi.org/10.3390/sym12122055
AMA Style
Bystrzanowska M, Tobiszewski M. Chemometrics for Selection, Prediction, and Classification of Sustainable Solutions for Green Chemistry—A Review. Symmetry. 2020; 12(12):2055. https://doi.org/10.3390/sym12122055
Chicago/Turabian StyleBystrzanowska, Marta, and Marek Tobiszewski. 2020. "Chemometrics for Selection, Prediction, and Classification of Sustainable Solutions for Green Chemistry—A Review" Symmetry 12, no. 12: 2055. https://doi.org/10.3390/sym12122055
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