目的 构建一种药瓶的分类及预测模型。方法 利用差分拉曼光谱和X射线荧光光谱对54个不同品牌和产地的塑料药瓶进行分析检验。结果 得到了54个样品的差分拉曼谱图及Cl、Ca、Ti、Fe、Zn等元素的含量。利用主成分分析对差分拉曼光谱数据进行降维，再利用系统聚类将降维后的数据分为8类，并以此为依据建立判别分析模型，最终判别模型经交叉验证可知准确率达到90.7%，多层感知器的分类准确率为100%，分类效果较好。结论 差分拉曼光谱可以根据谱图中的特征峰推断样品的分子结构，并且可以根据峰位对样品进行分类，并建立分析模型，X射线荧光光谱可以通过各元素的种类和含量的不同对样品进行区分，实现组内的细化。差分拉曼光谱和X射线荧光光谱可以分别从有机和无机的角度对药瓶进行分类，在分析上可以优势互补，可为公安机关实际办案探索出一种新的光谱联用角度和方法。

The work aims to construct a classification and prediction model of medicine bottles. Differential Raman spectroscopy and X-ray fluorescence spectroscopy were used to analyze and test 54 plastic medicine bottles from different brands and producing areas. The differential Raman spectra of 54 samples and the content of chlorine, calcium, titanium, iron and zinc were obtained. The principal component analysis was used to reduce the dimension of the differential Raman spectrum data, and then the system clustering was used to divide the reduced dimension data into 8 categories. Based on this, the discriminant analysis model was established. The final discriminant model was verified by cross validation, and the accuracy was 90.7%, and the classification accuracy of the multi-layer perceptron was 100%. The classification effect was good. The differential Raman spectrum can infer the molecular structure of the sample according to the characteristic peaks in the spectrum, and the samples can be classified and analyzed according to the peak position. The X-ray fluorescence spectrum can distinguish the samples according to the types and contents of various elements, so as to realize the refinement in the group. Differential Raman spectroscopy and X-ray fluorescence spectroscopy can classify medicine bottles from organic and inorganic perspectives respectively. They complement each other in analysis. They can be used to explore a new angle and method of spectral combination for the actual handling of cases by public security organs.