基于广义回归神经网络的微生物降解效应对助燃剂检测的影响

doi:10.3724/SP.J.1123.2018.12035

摘要/Abstract

摘要：

为探究火场土壤载体中微生物降解效应对助燃剂鉴定的影响，在普通土和培养土两种土样上注射助燃剂，以密封存放时间为变量，通过静态顶空的样品预处理方式对样品内的助燃剂残留物进行气相色谱-质谱法（GC-MS）鉴定。研究发现，微生物降解效应会改变样品内助燃剂组分，不同土样内降解结果有所不同，普通土样的降解效应较培养土样明显，C9~C12直链烷烃和单取代芳香烃更易被降解，多取代芳烃的降解难度随取代基含量的增多而增加。按土样种类采用主成分分析（PCA）的方式进行数据降维后，采用广义回归神经网络（GRNN）对不同土样结果区分，准确率达100%。

关键词: 广义回归神经网络, 气相色谱-质谱, 微生物降解, 主成分分析, 助燃剂

Abstract:

To explore the effects of microbial degradation at fire scenes on the identification of accelerants, samples of general and nutrient soil were injected into an accelerant, considering sealed storage time as a variable. The accelerant residues in the samples were identified through gas chromatography-mass spectrometry (GC-MS) with passive headspace pretreatment. The result indicated that the composition of the accelerants was changed due to microbial degradation, with varying degrees of degradation degree in different soil samples. Degradation in the general soil samples was superior to that in nutrient soil samples. C9 to C12 n-alkanes and monosubstituted aromatic hydrocarbons were more easily degraded, compared to other components. With the increase of substituents, the degradation of polysubstituted aromatic hydrocarbons became increasingly difficult. Principal component analysis (PCA) was used for data dimension reduction based on the type of soil samples. Generalized regression neural network (GRNN) analysis classified the soil samples with 100% accuracy.

Key words: accelerant, gas chromatography-mass spectrometry (GC-MS), generalized regression neural network (GRNN), microbial degradation, principal component analysis (PCA)

中图分类号:

O658

方强, 刘玲. 基于广义回归神经网络的微生物降解效应对助燃剂检测的影响[J]. 色谱, 2019, 37(6): 655-660.

FANG Qiang, LIU Ling. Effects of microbial degradation effects based on generalized regression neural network on the detection of accelerants[J]. Chinese Journal of Chromatography, 2019, 37(6): 655-660.

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