This article contains a comprehensive tutorial on classification by means of Soft Independent Modelling of Class Analogy (SIMCA). Such a tutorial was conceived in an attempt to offer pragmatic guidelines for a sensible and correct utilisation of this tool as well as answers to three basic questions: “why employing SIMCA?”, “when employing SIMCA?” and “how employing/not employing SIMCA?”. With this purpose in mind, the following points are here addressed: i) the mathematical and statistical fundamentals of the SIMCA approach are presented; ii) distinct variants of the original SIMCA algorithm are thoroughly described and compared in two different case-studies; iii) a flowchart outlining how to fine-tune the parameters of a SIMCA model for achieving an optimal performance is provided; iv) figures of merit and graphical tools for SIMCA model assessment are illustrated and v) computational details and rational suggestions about SIMCA model validation are given. Moreover, a novel Matlab toolbox, which encompasses routines and functions for running and contrasting all the aforementioned SIMCA versions is also made available.

Class modelling by Soft Independent Modelling of Class Analogy: why, when, how? A tutorial / Vitale, R.; Cocchi, M.; Biancolillo, A.; Ruckebusch, C.; Marini, F.. - In: ANALYTICA CHIMICA ACTA. - ISSN 0003-2670. - 1270:(2023), pp. 1-19. [10.1016/j.aca.2023.341304]

Class modelling by Soft Independent Modelling of Class Analogy: why, when, how? A tutorial

Marini F.
Ultimo
2023

Abstract

This article contains a comprehensive tutorial on classification by means of Soft Independent Modelling of Class Analogy (SIMCA). Such a tutorial was conceived in an attempt to offer pragmatic guidelines for a sensible and correct utilisation of this tool as well as answers to three basic questions: “why employing SIMCA?”, “when employing SIMCA?” and “how employing/not employing SIMCA?”. With this purpose in mind, the following points are here addressed: i) the mathematical and statistical fundamentals of the SIMCA approach are presented; ii) distinct variants of the original SIMCA algorithm are thoroughly described and compared in two different case-studies; iii) a flowchart outlining how to fine-tune the parameters of a SIMCA model for achieving an optimal performance is provided; iv) figures of merit and graphical tools for SIMCA model assessment are illustrated and v) computational details and rational suggestions about SIMCA model validation are given. Moreover, a novel Matlab toolbox, which encompasses routines and functions for running and contrasting all the aforementioned SIMCA versions is also made available.
2023
class modelling (CM); orthogonal distance (OD); principal component analysis (PCA); score distance (SD); soft independent modelling of class analogy (SIMCA)
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Class modelling by Soft Independent Modelling of Class Analogy: why, when, how? A tutorial / Vitale, R.; Cocchi, M.; Biancolillo, A.; Ruckebusch, C.; Marini, F.. - In: ANALYTICA CHIMICA ACTA. - ISSN 0003-2670. - 1270:(2023), pp. 1-19. [10.1016/j.aca.2023.341304]
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11573/1687615
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