CAJ | 학술논문

在火灾调查中,检测汽油成分并对其进行正确分类尤为重要.运用GC-MS对90#和93#两种普通汽油的共50个样本进行检测,所得的GC-MS原始数据通过PCA方法进行处理,以提取有用信息,避免冗余变量进入后续计算.在此基础上应用KNN方法对这两种汽油助燃剂进行分类.结果表明, KNN方法对这两种汽油的分类准确率达到100%,且当初始数据未经标准化预处理时也能达到同样准确的分类效果.研究表明:将模式识别方法正确地运用到助燃剂鉴定和分类工作中有助于火灾调查.
재화재조사중,검측기유성분병대기진행정학분류우위중요.운용GC-MS대90#화93#량충보통기유적공50개양본진행검측,소득적GC-MS원시수거통과PCA방법진행처리,이제취유용신식,피면용여변량진입후속계산.재차기출상응용KNN방법대저량충기유조연제진행분류.결과표명, KNN방법대저량충기유적분류준학솔체도100%,차당초시수거미경표준화예처리시야능체도동양준학적분류효과.연구표명:장모식식별방법정학지운용도조연제감정화분류공작중유조우화재조사.
Detection and accurate classification of gasoline is very important in fire investigation. In this paper, a total of 50 samples of regular gasoline, covering two different grades (90# and 93 #), were examined by gas chromatography-mass spectrometry (GC-MS). The GC-MS data were treated by Principal Component Analysis (PCA) to distill the information from the original dataset in order to avoid the redundant variables to be calculated. And k-nearest neighbors algorithm (KNN) was further applied to classify the two types of accelerant. The results showed that KNN could classify the two types of gasoline effectively, with the 100% probability (no prediction error), whether the data were normalized or not. The results indicated that the proper application of pattern recognition to the identification and classification of accelerant provided positive help in fire investigation.