Prediction Method for Production Year of Antai Pills Based on Near Infrared Spectroscopy

With the increase of the storage time of traditional Chinese medicine, the content of its effective components gradually decreases. Chemical detection means to consume the samples, with a long period and a high cost. In this paper, near infrared spectroscopy was used to identify the years of Antai pills of the classical prescriptions with different years. In order to explore the feasibility of this nondestructive and rapid quality control method, the absorbance data of 105 samples in 1 000 similar to 1 799 nm band near infrared spectroscopy of three years were collected, 80 samples were randomly selected as training sets and 25 samples as test sets. Firstly, the Successive Projection Algorithm (SPA) was adopted to eliminate the redundant information in the original spectral data, and the full input spectrum was optimized and the dimensionality was reduced. According to the internal of the test sets, the error value of the root mean square was cross-verified, 11 characteristic wavelengths were extracted from 800 wavelengths, respectively : (1 692, 1 714, 1 405, 1 001, 1 114, 1 478, 1 514, 1 788, 1 202, 1 014, 1 164) nm. Then the Support Vector Machine (SVM) classification model was established. Since the selection of the parameters in SVM model has a great influence on the classification accuracy, the Particle Swarm Optimization (PSO) algorithm was used to optimize the penalty parameter C and the kernel function parametersin SVM model to form PSOSVM classification model. Finally, after SPA dimension reduction, the characteristic wavelength was input into PSOSVM classification algorithm. Matlab software was used in the simulation test, and SVM, SPA-SVM classification models and SPA-PSOSVM classification model in this paper were respectively constructed. The classification test accuracy reached 76% , 92% and 100% respectively. From the simulation results, it can be seen that the SPA wavelength optimization could effectively reduce the redundant spectral information and reduce the time required for modeling. The PSO-SVM classification model, the complexity of the model was reduced and the classification accuracy was improved. The results show that the near infrared algorithm established in this paper could accurately and nondestructively distinguish the production years of the traditional Chinese medicine Antai pills, and this study could provide a way of thinking for the evaluation of the differences of the years of traditional Chinese medicine.