Issue |
J. Eur. Opt. Soc.-Rapid Publ.
Volume 11, 2016
|
|
---|---|---|
Article Number | 16006i | |
Number of page(s) | 20 | |
DOI | https://doi.org/10.2971/jeos.2016.16006i | |
Published online | 06 February 2016 |
Regular paper – Invited publication
Statistical classification of soft solder alloys by laser-induced breakdown spectroscopy: review of methods
Faculty of Electronics, Wroclaw University of Technology, Wroclaw, Poland
Received:
8
October
2015
Accepted:
12
January
2016
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.
Key words: Laser-induced breakdown spectroscopy / soft solder alloys / supervised classification / principal component analysis / machine learning methods
© The Author(s) 2016. All rights reserved.
Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.
Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.
Initial download of the metrics may take a while.