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多种基质上残留混合物中生物和化学威胁模拟物的鉴别。

Discrimination of biological and chemical threat simulants in residue mixtures on multiple substrates.

机构信息

US Army Research Laboratory, RDRL-WML-B, Aberdeen Proving Ground, Aberdeen, MD 21005, USA.

出版信息

Anal Bioanal Chem. 2011 Jul;400(10):3289-301. doi: 10.1007/s00216-011-4746-4. Epub 2011 Feb 18.

Abstract

The potential of laser-induced breakdown spectroscopy (LIBS) to discriminate biological and chemical threat simulant residues prepared on multiple substrates and in the presence of interferents has been explored. The simulant samples tested include Bacillus atrophaeus spores, Escherichia coli, MS-2 bacteriophage, α-hemolysin from Staphylococcus aureus, 2-chloroethyl ethyl sulfide, and dimethyl methylphosphonate. The residue samples were prepared on polycarbonate, stainless steel and aluminum foil substrates by Battelle Eastern Science and Technology Center. LIBS spectra were collected by Battelle on a portable LIBS instrument developed by A3 Technologies. This paper presents the chemometric analysis of the LIBS spectra using partial least-squares discriminant analysis (PLS-DA). The performance of PLS-DA models developed based on the full LIBS spectra, and selected emission intensities and ratios have been compared. The full-spectra models generally provided better classification results based on the inclusion of substrate emission features; however, the intensity/ratio models were able to correctly identify more types of simulant residues in the presence of interferents. The fusion of the two types of PLS-DA models resulted in a significant improvement in classification performance for models built using multiple substrates. In addition to identifying the major components of residue mixtures, minor components such as growth media and solvents can be identified with an appropriately designed PLS-DA model.

摘要

已经探索了激光诱导击穿光谱(LIBS)在存在干扰物的情况下区分多种基质上制备的生物和化学威胁模拟残留物的潜力。测试的模拟样品包括萎缩芽孢杆菌孢子、大肠杆菌、MS-2 噬菌体、金黄色葡萄球菌α-溶血素、2-氯乙基乙基硫醚和二甲基甲基膦酸酯。残留物样品由 Battelle 东部科学技术中心在聚碳酸酯、不锈钢和铝箔基质上制备。LIBS 光谱由 Battelle 使用 A3 技术公司开发的便携式 LIBS 仪器采集。本文介绍了使用偏最小二乘判别分析(PLS-DA)对 LIBS 光谱进行的化学计量分析。比较了基于全 LIBS 光谱、选定发射强度和比的 PLS-DA 模型的性能。基于包括基质发射特征的全光谱模型通常提供了更好的分类结果;然而,在存在干扰物的情况下,强度/比模型能够正确识别更多类型的模拟残留物。两种类型的 PLS-DA 模型的融合导致使用多种基质构建的模型的分类性能显著提高。除了识别残留物混合物的主要成分外,还可以使用适当设计的 PLS-DA 模型识别生长培养基和溶剂等微量成分。

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