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

Nataly J. Galan-Freyle , Amanda M. Figueroa-Navedo , Yahn C. Pacheco-Londoño , William Ortiz-Rivera , Leonardo C. Pacheco-Londoño , Samuel P. Hernández-Rivera
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引用次数: 10

Abstract

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

Abstract Image

化学计量学-增强光纤拉曼检测,区分和定量的化学试剂模拟隐藏在商业瓶
利用偏最小二乘结合判别分析(PLS-DA)和人工神经网络(ANN)分析等化学计量学技术,提高了对化学战剂模拟物的检测、判别和定量。利用光纤耦合拉曼光谱分析了磷酸三乙酯(TEP)与商业产品在原始容器中的混合。实验采用定制的光纤探针,工作波长为488nm。检测是用商业瓶子和水的混合物完成的。瓶的材质有绿色塑料、绿色玻璃、透明塑料、透明玻璃、琥珀色玻璃和白色塑料。考虑到一些瓶子材料的低散射峰强度,积分时间增加了。短的整合时间没有提供琥珀玻璃和白色塑料的信息。检出限为1-5%,取决于瓶的材质和含量。当从来自同一类型瓶子材料的数据集生成模型时,PLS-DA实现了良好的识别。当使用大数据集时,ANN表现更好,从瓶子材料和内容物中区分TEP,并准确分类超过90%的数据。
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