无监督化学计量分析和自组织特征映射（SOFM）在轻燃料分类中的应用。
Application of unsupervised chemometric analysis and self-organizing feature map (SOFM) for the classification of lighter fuels.
机构信息
Centre for Forensic Science, Department of Pure and Applied Chemistry, University of Strathclyde, 204 George Street, Glasgow G1 1WX.
出版信息
Anal Chem. 2010 Aug 1;82(15):6395-400. doi: 10.1021/ac100381a.
A variety of lighter fuel samples from different manufacturers (both unevaporated and evaporated) were analyzed using conventional gas chromatography-mass spectrometry (GC-MS) analysis. In total 51 characteristic peaks were selected as variables and subjected to data preprocessing prior to subsequent analysis using unsupervised chemometric analysis (PCA and HCA) and a SOFM artificial neural network. The results obtained revealed that SOFM acted as a powerful means of evaluating and linking degraded ignitable liquid sample data to their parent unevaporated liquids.
使用常规气相色谱-质谱联用技术（GC-MS）分析了来自不同制造商的各种打火机燃料样本（包括未蒸发和已蒸发的样本）。共选择了 51 个特征峰作为变量，并在使用无监督化学计量分析（PCA 和 HCA）和 SOFM 人工神经网络进行后续分析之前进行数据预处理。结果表明，SOFM 是一种强大的手段，可以评估和将降解的可燃液体样本数据与其原始未蒸发液体相关联。
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