Abstract
This paper reviews machine-learning methods that are nowadays the most frequently used for the supervised classification of spectral signals in laser-induced breakdown spectroscopy (LIBS). We analyze and compare various statistical classification methods, such as linear discriminant analysis (LDA), quadratic discriminant analysis (QDA), partial least-squares discriminant analysis (PLS-DA), soft independent modeling of class analogy (SIMCA), support vector machine (SVM), naive Bayes method, probabilistic neural networks (PNN), and K-nearest neighbor (KNN) method. The theoretical considerations are supported with experiments conducted for real soft-solder-alloy spectra obtained using LIBS. We consider two decision problems: binary and multiclass classification. The former is used to distinguish overheated soft solders from their normal versions. The latter aims to assign a testing sample to a given group of materials. The measurements are obtained for several laser-energy values, projection masks, and numbers of laser shots. Using cross-validation, we evaluate the above classification methods in terms of their usefulness in solving both classification problems.
Highlights
The most popular method of connecting electronic components on printed circuit boards (PCBs) is soft soldering
The results show that the tested algorithms can be assigned to four groups on the basis of their performance, and they are ordered in the decreasing order of performance as follows: (1) soft independent modeling of class analogy (SIMCA); (2) linear discriminant analysis (LDA), support vector machine (SVM), and partial least-squares discriminant analysis (PLS-DA); (3) quadratic discriminant analysis (QDA) and naive Bayes (NB); (4) the K-nearest neighbor (KNN) family and probabilistic neural networks (PNN)
Experiments based on laser-induced breakdown spectroscopy (LIBS) observations showed that SIMCA outperforms the other algorithms for both classification problems
Summary
Faculty of Electronics, Wroclaw University of Technology, Wroclaw, Poland Faculty of Electronics, Wroclaw University of Technology, Wroclaw, Poland Faculty of Electronics, Wroclaw University of Technology, Wroclaw, Poland. This paper reviews machine-learning methods that are nowadays the most frequently used for the supervised classification of spectral signals in laser-induced breakdown spectroscopy (LIBS). We analyze and compare various statistical classification methods, such as linear discriminant analysis (LDA), quadratic discriminant analysis (QDA), partial least-squares discriminant analysis (PLS-DA), soft independent modeling of class analogy (SIMCA), support vector machine (SVM), naive Bayes method, probabilistic neural networks (PNN), and K-nearest neighbor (KNN) method. The theoretical considerations are supported with experiments conducted for real soft-solder-alloy spectra obtained using LIBS. We consider two decision problems: binary and multiclass classification. The former is used to distinguish overheated soft solders from their normal versions. The latter aims to assign a testing sample to a given group of materials.
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