Classification of Liquid Foundation Based on Microconfocal Raman Spectroscopy Combined with Machine Learning
In order to establish a rapid test method for foundation samples,confocal Raman spectroscopy and machine learning were used to classify different brands,colors and prices of foundation.Firstly,the Ra-man spectral data of 50 foundation samples were preprocessed by Savitzky-Golay smoothing and normalization algorithm.Secondly,eigenvalues were extracted by principal component analysis,and the first two principal components were extracted for subsequent research.The 50 samples were divided into 5 categories by K-Means clustering method,and the classification results were verified by systematic clustering method.Finally,a support vector machine(SVM)classification model was built with 40 samples as training set and 10 sam-ples as test set.The results show that the accuracy of SVM model training set and test set under Linear ker-nel function can reach 90％.It shows that this method can realize the automation of distinguishing the brand and price of foundation liquid,and provide a new idea for the public security organs'material evidence in-spection,conviction and punishment.
万方数据
维普
