重质矿物油的检验分析在交通肇事案件处理过程中具有重要作用。为了实现对重质矿物油种类的准确区分,本文采集了汽机油、柴机油、润滑脂、齿轮油和液压油5种重质矿物油共计120份样本的红外光谱和拉曼光谱数据,结合光谱融合的相关方法,建立了基于支持向量机(SVM)的重质矿物油分类判别模型。实验结果表明:使用单一光谱数据进行建模分类的准确率较低;对初级光谱融合数据进行建模分析时,5种重质矿物油的分类识别准确率稍高于前者,最高可达75%;使用中级光谱融合数据结合主成分分析建模能够实现5种重质矿物油的完全区分,在26维矩阵上特征提取得最好,分类识别率为100%。使用光谱数据融合结合SVM建模分析,能够实现重质矿物油的完全区分,该方法提高了检验鉴定效率,能够满足公安机关提出的快速、准确的检验要求,为基层民警处理相关案件提供了理论支撑和方法参考。

The inspection and analysis of heavy mineral oil plays an important role in dealing with traffic-accident cases. In order to obtain accurate classifications of heavy mineral oils, we collected infrared and Raman spectral data for 120 samples of five kinds of heavy mineral oils, including gasoline engine oil, diesel engine oil, grease, gear oil, and hydraulic oil. We established a classification and discrimination model for heavy mineral oil by using a support vector machine (SVM) combined with a spectral-fusion method. The results showed the accuracy of modeling classification using single-spectrum data to be rather low. When we modeled and analyzed the data obtained from primary spectral fusion, the classification and recognition rates for the five heavy mineral oils were slightly better, with an accuracy up to 75%. However, modeling that used data from intermediate spectral fusion combined with principal component analysis achieved complete differentiation among the five heavy mineral oils, with feature extraction from the 26-dimensional matrix being the best, with an accuracy up to 100%. In summary, spectral-fusion data combined with SVM modeling analysis can achieve complete separation among heavy mineral oils. The method improves the efficiency of inspection and identification, which fulfills the goal of rapid and accurate inspection for frontline law-enforcement personnel. It also provides theoretical support and a reference method for relevant cases.