Citation:

Cisewski, J., E. SNYDER, J. Hannig, AND L. OUDEJANS. Support vector machine classification of suspect powders using laser induced breakdown spectroscopy (LIBS) spectral data . JOURNAL OF CHEMOMETRICS. John Wiley & Sons, Ltd., Indianapolis, IN, 26(5):i-iii, 135-208, (2012).

Impact/Purpose:

: Classification of suspect powders, to determine if they could be Bacillus anthracis containing spore powders, using Laser Induced Breakdown Spectroscopy (LIBS) spectra is difficult due to the variability in composition of these suspect powders 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, and then a linear model technique using the known elemental structure of the harmful substance was incorporated for dimension reduction, and finally a support vector machines approach was employed for the final classification of the substance. The method was applied to real-data produced from a LIBS device. Several methods used to test the predictive performance of the classification model revealed promising results.

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Journal Article

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