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Chemometrics for Selection, Prediction, and Classification of Sustainable Solutions for Green Chemistry—A Review
Abstrakt
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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- Accepted albo Published Version
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Informacje szczegółowe
- Kategoria:
- Publikacja w czasopiśmie
- Typ:
- artykuły w czasopismach
- Opublikowano w:
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Symmetry-Basel
nr 12,
ISSN: 2073-8994 - Język:
- angielski
- Rok wydania:
- 2020
- Opis bibliograficzny:
- Bystrzanowska M., Tobiszewski M.: Chemometrics for Selection, Prediction, and Classification of Sustainable Solutions for Green Chemistry—A Review// Symmetry-Basel -Vol. 12,iss. 12 (2020), s.2055-
- DOI:
- Cyfrowy identyfikator dokumentu elektronicznego (otwiera się w nowej karcie) 10.3390/sym12122055
- Weryfikacja:
- Politechnika Gdańska
wyświetlono 156 razy
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