目的 建立差分拉曼光谱用于无损识别白色购物纸袋的方法。方法 对收集到的60种不同品牌、不同规格的白色购物纸袋进行拉曼光谱测定，对样品的拉曼光谱图进行预处理，根据光谱图对样品进行初步分类，并结合化学计量方法对样品进行分组。应用Fisher判别分析方法对分类结果进行验证。最后应用RBF模型对未知样本进行分类判别。结果 结合样品中所含的碳酸钙、滑石粉、硫酸钡的不同，可初步将白色购物纸袋样品分为五大类，采用K-均值聚类方法继续细分，通过Fisher判别方法对样品分结果进行验证，判别准确率为100%。应用神经网络RBF模型对未知样本进行判别分析，准确率达到89.48%。结论 该方法简便易行，为白色购物纸袋的分类提供了科学的依据，也为公安基层工作的开展提供了便捷的办法。

The work aims to establish a method for non-destructive identification of white shopping paper bags by differential Raman spectroscopy. 60 white shopping paper bags of different brands and specifications were collected and detected by Raman spectroscopy. The Raman spectra of the samples were preprocessed, and the samples were preliminarily classified based on the spectra. The samples were grouped by stoichiometric methods. In addition, Fisher discriminant analysis method was applied to verify the classification results. Finally, the RBF model was applied to classify and distinguish unknown samples. Based on the difference in calcium carbonate, talc powder, and barium sulfate contained in the samples, the white shopping paper bag samples could be preliminarily divided into five categories. K-means clustering method was used for further subdivision, and Fisher discriminant method was used to verify the sample classification results, with a discriminant accuracy of 100%. The application of neural network RBF model for discriminative analysis of unknown samples achieved an accuracy of 89.48%. This method is simple and easy to implement, which provides a scientific basis for the classification of white shopping paper bags, and also provides a convenient way for the development of grassroots public security work.