Objective: To establish the pattern discernment model with the use of near-infrared reflectance spectroscopy (NIRS)

principal component analysis (PCA) and support vector machine (SVM) algorithms for rapid identification of Benzoinum

Succinum

Myrrha

Olibanum

Colophonium and Bambusae Concretio Silicea

all of which are the traditional Chinese medicines (TCMs) containing resins and other components. Method: According to morphological identification and conventional physical and chemical analysis on the above 6 kinds of samples collected form major national medicinal materials markets

a total of 55 batches of samples were verified as genuine medicinal herbs. These samples were smashed into uniform powders

and the NIR spectra of sample powder were scanned at 4 000-12 000 cm-1. The characteristic bands at 9 000-5 400

5 000-4 000 cm-1 were used to establish models for pre-treatment by vector normalization (VN)

first derivative (FD) and second derivative (SD) respectively

and in addition

PCA dimension reduction was also conducted. The best pre-treatment method was selected according to the three-dimensional scatter diagram of principal components

and the PCA dimension reduction data after best pre-treatment method were used to establish the pattern discernment models based on the SVM algorithm. The SVM model parameters c and g were optimized by using grid search method combined with 5-fold cross validation method. By comparing the forecast accuracy of PCA-SVM models established with different number of principal components

the best number was optimized and finally

NIR rapid identification model was established for 6 Chinese herbs. Result: At 9 000-5 400

5 000-4 000 cm-1

SD was determined as the best pre-treatment method

and after SD pre-treatment for PCA dimension reduction

the best number of principal components was determined as 3

with an accumulative contribution rate of 93.57%. According to the grid search method

c=65 536

g=512

which were the optimum parameters of the SVM model. In the established PCA-SVM models

the forecast accuracy was 100% for both training set and verification set

and the forecast accuracy was also 100% for 5-fold cross validation method. Conclusion: The NIRS identification model based on PCA-SVM had high forecast accuracy and strong prediction ability

which can be used for rapid

nondestructive and accurate identification of above 6 kinds of TCMs when it was used in combination with the advantages of NIRS.