References



Toolbox reference

The toolbox is freeware and may be used if proper reference is given to the authors. Preferably refer to the following paper:

Ballabio D, Consonni V, (2013) Classification tools in chemistry. Part 1: Linear models. PLS-DA. Analytical Methods, 5, 3790-3798 [link]

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General references for classification methods

An excellent comprehensive reference to multivariate classification methods is The Elements of Statistical Learning: Data Mining, Inference, and Prediction (2009), by Trevor Hastie, Robert Tibshirani and Jerome Friedman (available on line here).

A tutorial on PLSDA is available in the following paper: Ballabio D, Consonni V, (2013) Classification tools in chemistry. Part 1: Linear models. PLS-DA. Analytical Methods, 5, 3790-3798.

A review on one-class classifiers (including SIMCA): R. Brereton, One-class classifiers, Journal of Chemometrics, 2011, 25, 225-246 [link] . A tutorial on PCA (which is the basis for SIMCA) can be found here: R. Bro, A.K. Smilde, Principal component analysis, Analytical Methods, 2014,6, 2812-2831.

Details on SVM can be found here: The Elements of Statistical Learning: Data Mining, Inference, and Prediction (2009), by Trevor Hastie, Robert Tibshirani and Jerome Friedman (available on line here).

Details on Potential Functions can be found here: D. Coomans, D.L. Massart, Potential Methods in pattern recognition. Part 1. Classification Aspects of the Supervised Method ALLOC. Analytica Chimica Acta, 133 (1981) 215-224. M. Forina, C. Armanino, R. Leardi, G. Drava, A class-modelling technique based on potential functions, Journal of Chemometrics, 5 (1991) 435-453.

Details on UNEQ can be found here: P. Oliveri (2017) Class-modelling in food analytical chemistry: Development, sampling, optimisation and validation issues - A tutorial. Analytica Chimica Acta, 982, 9-19

Details on Backpropagation Neural Networks can be found here: B.J. Wythoff, Backpropagation neural networks: A tutorial, Chemometrics and Intelligent Laboratory Systems (1993), 18, 115-155

Details on classification measures can be found here: Ballabio, D., Grisoni, F., Todeschini, R. (2018). Multivariate comparison of classification performance measures. Chemometrics and Intelligent Laboratory Systems, 174, 33-44.

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