Indirect quantitative structure-retention relationship for steroid identification: A chemometric challenge at “Chimiométrie 2016”

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• 作者：Giuseppe Marco Randazzo^a ; ^b ; Evelyne Vigneau^c ; Philippe Courcoux^c ; Corentin Harrouet^d ; Yves Lijour^d ; Pierre Dardenne^e ; Julien Boccard^a ; ^b ; Serge Rudaz^a ; ^b ; ^{serge.rudaz@unige.ch}
• 关键词：log kW ; LSS log kW parameter ; S ; LSS S parameter ; tR ; Retention time ; PCA ; Principal component analysis ; MLR ; Multiple linear regression ; PLS ; Partial least squares ; RF ; Random forest ; SVR ; Support vector regression ; ANN ; Artificial neural network ; RMSEC ; Root mean square error of calibration
• 刊名：Chemometrics and Intelligent Laboratory Systems
• 出版年：2017
• 出版时间：15 January 2017
• 年：2017
• 卷：160
• 期：Complete
• 页码：52-58
• 全文大小：683 K
• 卷排序：160

文摘

Accurate retention time prediction is essential for steroid isomers identification. Quantitative Structure-Retention Relationship constitutes a very potent approach. Indirect retention time prediction can be used in any gradient conditions. The best results of the "Chimiométrie 2016" challenge are presented.