Chemometrics-enhanced fiber optic Raman detection, discrimination and quantification of chemical agents simulants concealed in commercial bottles

摘要: Abstract Chemometric techniques such as partial least squares combined with discriminant analysis (PLS–DA) and artificial neural networks (ANN) were used to enhance the detection, discrimination quantification of chemical warfare agents simulants. Triethyl phosphate (TEP) mixed commercial products in their original containers was analyzed through container walls using fiber-optic-coupled Raman spectroscopy. Experiments performed by employing a custom built optical fiber probe operating at 488 nm. Detection accomplished mixtures contents bottles water. The bottle materials included green plastic, glass, clear amber glass white plastic. To account for low scattering-peak intensities some materials, integration times increased. Short provided no information limits detection on order 1–5%, depending contents. Good achieved PLS–DA when models generated from dataset originating same type material. ANN better large sets data used, discriminating TEP contents, well accurately classifying over 90% data.