Comparison and optimization of rapid and nondestructive qualitative analysis models based on fourier transform infrared spectroscopy information of drugs

Abstract

It is a key issue in the field of drug prevention and control to realize the rapid and nondestructive qualitative analysis of drugs. In this study, spectral data of 196 pieces of drug samples based on actual cases which are three types in total, were sorted out. The Bayesian discriminant analysis (BDA) models were constructed based on full spectral, fingerprint spectral and functional group spectral data sets were extracted by principal component analysis (PCA). The classification results for different types of data sets were compared, and the optimal spectral region were selected. In the BDA, the model based on the data of the fingerprint spectra the highest classification accuracy, and the classification accuracy of the sample training set based on three types of drugs was 0.837. The identification models based on data sets of fingerprint spectra were established by multilayer perceptron (MLP) and support vector machines (SVM), as well as compare the model parameters of SVM models based on different kernel functions. According to the results, it shows that in the three types of drugs recognition and classification process, the SVM model based on linear kernel functions has achieved the best classification effect, with the overall classification accuracy of the sample training set reaches 0.849, and the model can fully classify drugs of the same type. The classification results were ideal for the SVM model based on linear kernel functions for fingerprint spectral data set. The results of this study demonstrate the potential of Fourier transform infrared spectroscopy in combination with the chemometrics methods of SVM as a new method for the identification of different types and different kinds of drugs of the same type.