论文信息 - Classification tools in chemistry. Part 1: linear models. PLS-DA

Classification tools in chemistry. Part 1: linear models. PLS-DA

The common steps to calibrate and validate classification models based on partial least squares discriminant analysis are discussed in the present tutorial. All issues to be evaluated during model training and validation are introduced and explained using a chemical dataset, composed of toxic and non-toxic sediment samples. The analysis was carried out with MATLAB routines, which are available in the ESI of this tutorial, together with the dataset and a detailed list of all MATLAB instructions used for the analysis.

D. Ballabio | V. Consonni

[1] L. A. Stone,et al. Computer Aided Design of Experiments , 1969 .

[2] A. Höskuldsson. PLS regression methods , 1988 .

[3] Roberto Todeschini,et al. A new algorithm for optimal, distance based, experimental design , 1992 .

[4] Jure Zupan,et al. Kohonen and counterpropagation artificial neural networks in analytical chemistry , 1997 .

[5] A. Tropsha,et al. Beware of q2! , 2002, Journal of molecular graphics & modelling.

[6] R. Boqué,et al. Calculation of the reliability of classification in discriminant partial least-squares binary classification , 2009 .

[7] José Manuel Amigo,et al. A chemometric approach to the environmental problem of predicting toxicity in contaminated sediments , 2010 .

[8] José Manuel Amigo,et al. Development of models for predicting toxicity from sediment chemistry by partial least squares-discriminant analysis and counter-propagation artificial neural networks. , 2010, Environmental pollution.