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QSPR modeling of nonionic surfactant cloud points: An update by Yueying Ren; Baowei Zhao; Qing Chang; Xiaojun Yao is a Chemistry article available to read on EtoBox.

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Quantitative structure-property relationship (QSPR) models for the cloud points of nonionic surfactants were developed based on CODESSA descriptors. Essentials accounting for a reliable model were considered carefully. Four descriptors were selected by a generic algorithm (GA) method to link the structures of nonionic surfactants to their corresponding cloud-point values. The descriptors were also analyzed using principal component analysis (PCA). Nonlinear models based on support vector machine (SVM) and projection pursuit regression (PPR) were also developed. All models were validated in two ways, i.e., internal cross-validation (CV) and a test set. The results are discussed in light of the main factors that influence the property under investigation and its modeling. In addition, an independent external data set of 16 nonionic surfactants was used to check the generalization ability of the optimum model.

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Author

Yueying Ren; Baowei Zhao; Qing Chang; Xiaojun Yao

Publisher

Elsevier Science; Elsevier ; Elsevier Inc.; Elsevier BV (ISSN 0021-9797)

Published

2011

Language

EN

Field

Chemistry (Physical Sciences)