拉曼光谱技术在法庭科学中有着广泛的应用。采用显微激光拉曼光谱分析技术和K近邻算法对25个塑钢窗样本进行研究。通过主成分分析提取到5个主成分,并运用训练样本为测试样本的方法进行交互验证。当K=1时,测试样本的出错率最低,以区分贡献值最高的三个特征变量为参数建立分类模型,实现了对未知变量的准确归类,模型总分类准确率可达71%,区分效果良好,比直接通过谱图比较得到的结论更加准确。

Raman spectroscopy has been used in forensic science widely. In this paper, laser Raman spectroscopy analysis technology and K nearest neighbors algorithm are used to study 25 plastic steel window samples. The five principal components are extracted by principal component analysis, and the experiment built interactive verification test with the method regarding the training sample as the test sample. When the K value equals to 1, the lowest training sample error rate appears. Taking the three highest contribution value characteristic variables as parameters to build the classification model to realize the accurate classification of the unknown variables, and the total correct rate is 71%. The above method is more accurate than the direct observation of the spectra.