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

• Published in: Analytica chimica acta 2023-08, Vol.1270, p.341304-341304, Article 341304
• Main Authors: Vitale, Raffaele, Cocchi, Marina, Biancolillo, Alessandra, Ruckebusch, Cyril, Marini, Federico
• Format: Article
• Language: English
• Subjects: Chemical Sciences; class modelling (CM); or physical chemistry; Orthogonal Distance (OD); Principal Component Analysis (PCA); Score Distance (SD); Soft Independent Modelling of Class Analogy (SIMCA); Theoretical and
• ISSN: 0003-2670; 1873-4324
• DOI: 10.1016/j.aca.2023.341304

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. [Display omitted] •A tutorial on Soft Independent Modelling of Class Analogy (SIMCA).•Practical guidelines about why, when and how/how not employing SIMCA.•A comparison among four distinct variants of the original SIMCA algorithm.•A flowchart on how to fine-tune SIMCA model parameters.•Rational suggestions about SIMCA model assessment and validation.