Pearman William F, Fountain Augustus W
Photonics Research Center, Department of Chemistry and Life Science, United States Military Academy, West Point, New York 10996, USA.
Appl Spectrosc. 2006 Apr;60(4):356-65. doi: 10.1366/000370206776593744.
Initial results demonstrating the ability to classify surface-enhanced Raman (SERS) spectra of chemical and biological warfare agent simulants are presented. The spectra of two endospores (B. subtilis and B. atrophaeus), two chemical agent simulants (dimethyl methylphosphonate (DMMP) and diethyl methylphosphonate (DEMP)), and two toxin simulants (ovalbumin and horseradish peroxidase) were studied on multiple substrates fabricated from colloidal gold adsorbed onto a silanized quartz surface. The use of principal component analysis (PCA) and hierarchical clustering were used to evaluate the efficacy of identifying potential threat agents from their spectra collected on a single substrate. The use of partial least squares-discriminate analysis (PLS-DA) and soft independent modeling of class analogies (SIMCA) on a compilation of data from separate substrates, fabricated under identical conditions, demonstrates both the feasibility and the limitations of this technique for the identification of known but previously unclassified spectra.
本文展示了对化学和生物战剂模拟物的表面增强拉曼（SERS）光谱进行分类的初步结果。研究了两种芽孢（枯草芽孢杆菌和萎缩芽孢杆菌）、两种化学战剂模拟物（甲基膦酸二甲酯（DMMP）和甲基膦酸二乙酯（DEMP））以及两种毒素模拟物（卵清蛋白和辣根过氧化物酶）在由吸附在硅烷化石英表面的胶体金制成的多种基底上的光谱。主成分分析（PCA）和层次聚类被用于评估从在单个基底上收集的光谱中识别潜在威胁剂的效果。对在相同条件下制备的不同基底的数据汇编使用偏最小二乘判别分析（PLS-DA）和类类比软独立建模（SIMCA），证明了该技术用于识别已知但先前未分类光谱的可行性和局限性。
