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Chemometrics‐enhanced laser‐induced thermal emission detection of PETN and other explosives on various substrates
Author(s) -
FigueroaNavedo Amanda M.,
GalánFreyle Nataly J.,
PachecoLondoño Leonardo C.,
HernándezRivera Samuel P.
Publication year - 2015
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.2704
Subject(s) - pentaerythritol tetranitrate , chemometrics , principal component analysis , partial least squares regression , linear discriminant analysis , analytical chemistry (journal) , explosive material , biological system , chemistry , pattern recognition (psychology) , chromatography , artificial intelligence , computer science , machine learning , organic chemistry , biology
Infrared emissions (IREs) of samples of pentaerythritol tetranitrate (PETN) deposited as contamination residues on various substrates were measured to generate models for the detection and discrimination of the important nitrate ester from the emissions of the substrates. Mid‐infrared emissions were generated by heating the samples remotely using laser‐induced thermal emission (LITE). Chemometrics multivariate analysis techniques such as principal component analysis (PCA), soft independent modeling by class analogy (SIMCA), partial least squares‐discriminant analysis (PLS‐DA), support vector machines (SVMs), and neural network (NN) were employed to generate the models for the classification and discrimination of PETN IREs from substrate thermal emissions. PCA exhibited less variability for the LITE spectra of PETN/substrates. SIMCA was able to predict only 44.7% of all samples, while SVM proved to be the most effective statistical analysis routine, with a discrimination performance of 95%. PLS‐DA and NN achieved prediction accuracies of 94% and 88%, respectively. High sensitivity and specificity values were achieved for five of the seven substrates investigated. Copyright © 2015 John Wiley & Sons, Ltd.