Feature optimization in chemometric algorithms for explosives detection

Daniel W. Pinkham; James R. Bonick; Marc D. Woodka

doi:10.1117/12.923387

10 May 2012 Feature optimization in chemometric algorithms for explosives detection

Daniel W. Pinkham, James R. Bonick, Marc D. Woodka

Proceedings Volume 8357, Detection and Sensing of Mines, Explosive Objects, and Obscured Targets XVII; 83571K (2012) https://doi.org/10.1117/12.923387
Event: SPIE Defense, Security, and Sensing, 2012, Baltimore, Maryland, United States

Abstract

This paper details the use of a genetic algorithm (GA) as a method to preselect spectral feature variables for chemometric algorithms, using spectroscopic data gathered on explosive threat targets. The GA was applied to laserinduced breakdown spectroscopy (LIBS) and ultraviolet Raman spectroscopy (UVRS) data, in which the spectra consisted of approximately 10000 and 1000 distinct spectral values, respectively. The GA-selected variables were examined using two chemometric techniques: multi-class linear discriminant analysis (LDA) and support vector machines (SVM), and the performance from LDA and SVM was fed back to the GA through a fitness function evaluation. In each case, an optimal selection of features was achieved within 20 generations of the GA, with few improvements thereafter. The GA selected chemically significant signatures, such as oxygen and hydron peaks from LIBS spectra and characteristic Raman shifts for AN, TNT, and PETN. Successes documented herein suggest that this GA approach could be useful in analyzing spectroscopic data in complex environments, where the discriminating features of desired targets are not yet fully understood.

Citation Download Citation

Daniel W. Pinkham, James R. Bonick, and Marc D. Woodka "Feature optimization in chemometric algorithms for explosives detection", Proc. SPIE 8357, Detection and Sensing of Mines, Explosive Objects, and Obscured Targets XVII, 83571K (10 May 2012); https://doi.org/10.1117/12.923387

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