Chemometrics-enhanced laser-induced thermal emission detection of PETN and other explosives on various substrates

摘要: Infrared emissions (IREs) of samples pentaerythritol tetranitrate (PETN) deposited as contamination residues on various substrates were measured to generate models for the detection and discrimination important nitrate ester from substrates. Mid-infrared generated by heating remotely using laser-induced thermal emission (LITE). Chemometrics multivariate analysis techniques such principal component (PCA), soft independent modeling class analogy (SIMCA), partial least squares-discriminant (PLS-DA), support vector machines (SVMs), neural network (NN) employed classification PETN IREs substrate emissions. PCA exhibited less variability LITE spectra PETN/substrates. SIMCA was able predict only 44.7% all samples, while SVM proved be most effective statistical routine, with a performance 95%. PLS-DA NN achieved prediction accuracies 94% 88%, respectively. High sensitivity specificity values five seven investigated. Copyright © 2015 John Wiley & Sons, Ltd.