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• W3155403722 endingPage "119850" @default.
• W3155403722 startingPage "119850" @default.
• W3155403722 abstract "• The highest interpretability of selected variables was achieved by the SELECT or SPA algorithms. • The number of selected variables had a great impact on interpretability and model performance. • SELECT, SPA, and PCA appear promising for determining chemically important wavelengths. • The best classification performance was attained using PCA for dimension reduction. Near infrared (NIR) spectra are collected as a high amount of absorption values which usually greatly exceeds the sample size. Variable selection methods are employed in NIR spectroscopy to avoid “curse of dimensionality” related issues. In this paper, we examined the interpretability of selected variables, that is, how much selected wavelengths are related to the chemical structure of the materials studied, and if the relation is important for classification performance. Additionally, we examined classification performance in dependence on the number of selected variables. For this purpose, relative standard deviation (RSD), successive projection algorithm (SPA), stepwise decorrelation of variables (SELECT), genetic algorithm (GA), principal component analysis (PCA), and random (RANDOM) variable selection were applied in two-class classification modelling using linear discriminant analysis (LDA) or a support vector machine (SVM). Different pre-treatments and sample sizes were considered. Variable selection improved classification performance and variables selected by a majority of the methods considered were conveniently related to chemical structure. Interpretability and performance increase/decrease depend greatly on the number of selected variables, however. Since selected variables reveal great chemical interpretability, some variable selection methods could be employed to determine material characteristic absorption bands. SELECT and SPA displayed the best properties among the methods considered. To avoid faulty results, optimization of the number of selected variables should become the crucial stage in the variable selection process." @default.
• W3155403722 created "2021-04-26" @default.
• W3155403722 creator A5007264873 @default.
• W3155403722 date "2021-09-01" @default.
• W3155403722 modified "2023-10-16" @default.
• W3155403722 title "Interpretability of selected variables and performance comparison of variable selection methods in a polyethylene and polypropylene NIR classification task" @default.
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• W3155403722 doi "https://doi.org/10.1016/j.saa.2021.119850" @default.
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• W3155403722 hasPublicationYear "2021" @default.
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