Soft Independent Modeling of Class Analogy (SIMCA) Modeling of Laser-Induced Plasma Emission Spectra of Edible Salts for Accurate Classification

Abstract

We report soft independent modeling of class analogy (SIMCA) analysis of laser-induced plasma emission spectra of edible salts from 12 different geographical origins for their classification model. The spectra were recorded by using a simple laser-induced breakdown spectroscopy (LIBS) device. Each class was modeled by principal component analysis (PCA) of the LIBS spectra. For the classification of a separate test data set, the SIMCA model showed 97% accuracy in classification. An additional insight could be obtained by comparing the SIMCA classification result with that of partial least squares discriminant analysis (PLS-DA). Different from SIMCA, the PLS-DA classification accuracy seems to be sensitive to addition of new sample classes to the whole data set. This indicates that the individual modeling approach (SIMCA) can be an alternative to global modeling (PLS-DA), particularly for the classification problems with a relatively large number of sample classes.