利用差分拉曼光谱技术和决策树模型,建立了常见鞋底材料种类鉴别的分类模型,为鞋底材料种类鉴别提供了一种新方法。通过前期谱图预处理后,根据样本主要成分的不同对样本进行分类,然后利用主成分分析和层次聚类分析验证分类结果;基于分类结果建立分类树模型,最终对51个样本实现了整体分类正确率为98.0%的识别分类,经交叉验证后正确率达84.3%。这表明,利用差分拉曼光谱技术和决策树模型可以实现对鞋底样本光谱较为准确的识别与分类,为其他物证的区分鉴别提供了参考。

Using differential Raman spectroscopy and decision tree model, a classification model for identifying common shoe sole materials is established. This is a new method for identifying shoe sole materials. After preprocessing the pre-spectrum, the samples are classified according to the main components of the shoe sole, and then principal component and hierarchical clustering analyses are used to verify the classification results. Based on the classification results, a classification tree model is established, and finally 51 samples are recognized and classified with overall classification accuracy of 98.0% and the accuracy is 84.3% after cross-validation. These results show that using differential Raman spectroscopy and decision tree model can realize a more accurate identification and classification of shoe sole sample spectrum, providing a certain reference for the differentiation and identification of other physical evidence.