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. © 2013 The Royal Society of Chemistry.

Ballabio, D., Consonni, V. (2013). Classification tools in chemistry. Part 1: Linear models. PLS-DA. ANALYTICAL METHODS, 5, 3790-3798 [10.1039/c3ay40582f].

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

BALLABIO, DAVIDE;CONSONNI, VIVIANA
2013

Abstract

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. © 2013 The Royal Society of Chemistry.
Articolo in rivista - Articolo scientifico
classification, chemometrics, multivariate analysis, plsda, partial least square discriminant analysis
English
2013
5
3790
3798
none
Ballabio, D., Consonni, V. (2013). Classification tools in chemistry. Part 1: Linear models. PLS-DA. ANALYTICAL METHODS, 5, 3790-3798 [10.1039/c3ay40582f].
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/10281/45984
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