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Support vector machine classification of suspect powders using laser‐induced breakdown spectroscopy (LIBS) spectral data
Author(s) -
Cisewski Jessi,
Snyder Emily,
Hannig Jan,
Oudejans Lukas
Publication year - 2012
Publication title -
journal of chemometrics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.47
H-Index - 92
eISSN - 1099-128X
pISSN - 0886-9383
DOI - 10.1002/cem.2422
Subject(s) - laser induced breakdown spectroscopy , support vector machine , artificial intelligence , spectroscopy , pattern recognition (psychology) , linear discriminant analysis , computer science , dimensionality reduction , materials science , biological system , machine learning , physics , quantum mechanics , biology
Classification of suspect powders, by using laser‐induced breakdown spectroscopy (LIBS) spectra, to determine if they could contain Bacillus anthracis spores is difficult because of the variability in their composition and the variability typically associated with LIBS analysis. A method that builds a support vector machine classification model for such spectra relying on the known elemental composition of the Bacillus spores was developed. A wavelet transformation was incorporated in this method to allow for possible thresholding or standardization, then a linear model technique using the known elemental structure of the spores was incorporated for dimension reduction, and a support vector machine approach was employed for the final classification of the substance. The method was applied to real data produced from an LIBS device. Several methods used to test the predictive performance of the classification model revealed promising results. Published 2012. This article is a US Government work and is in the public domain in the USA.