Résumé
Objective: To establish a near-infrared diffuse reflectance spectroscopy(NIRS) identification model for crude products,counterfeit products and processed products of Calamina by principal component analysis(PCA) and support vector machine(SVM) algorithm. Method: NIRS of crude products,counterfeit products and processed products of Calamina were collected,the characteristic spectrum segments were selected,the preprocessing method and the optimum principal component number were optimized,and the PCA-SVM qualitative model was established. Result: The characteristic spectrum segment of analysis model was 7 500-4 000 cm-1.Spectra were preprocessed by the first-order derivative method(FD).The optimum principal component number was 5. And the optimum internal parameters of SVM[penalty factor(c)=0.25 and kernel function parameter(g)=8] were screened by applying the grid search algorithm.In the PCA-SVM qualitative model,the prediction accuracy rate was 100%for the 5-fold cross validation,and the prediction accuracy rates also were 100%both for training set and test set. Conclusion: PCA-SVM analysis model of NIRS for Calamina samples has a high prediction accuracy rate,and it can be used for the rapid and nondestructive identification of crude products,counterfeit products and processed products of Calamina by combining the diffuse reflection technique on solid powder.