This study was aimed to establish the classification method of Chinese herbal medicine based on feature parameters extracted from images of herbal transverse section, in order to explore the feasibility of automatic identi-fication method of herbal medicine. The extracted 26 parameters of 18 herbal medicine images by gray-level co-oc-currence matrix and grayscale gradient matrix were used as the basic data set. And the minimum covariance determi-nant (MCD) was used to delete the outliers. A total of 18 identification models were established using the native Bayes method and BP neural network methods. The results showed that the average correct rates of models were 90%. It was concluded the feasibility of using these models in the establishment of the automatic identification method of herbal medicines. It provided new technologies for the quantitative, scientific and objective identification of Chinese herbal medicine.

Objective To establish a high performance liquid chromatography (HPLC) method for the determination of baicalin and naringin inQinbei mixture.Methods The HPLC system consisted of the Fortis-C18(4.6 mm × 250 mm, 5μm) column, and the mobile phase consisted of MeOH:0.4% H3PO4 (42:58), and the flow rate was 1.0 ml/min, and the UV detector was set at 280 nm, and the column temperature was 30℃.Results The linear response range of baicalin was 0.062-0.930μg. The linear response range of naringin was 0.033-0.492μg. The average recovery of baicalin was 98.11% (RSD=1.62%). The average recovery of naringin was 96.78% (RSD=1.74%).Conclusions The method is simple, rapid, accurate and repeatable. It can be applied in determination of baicalin and naringin inQinbei mixture.

This article investigated the applicability of the near infrared spectroscopy (NIR) technology, combined with the least squares support vector machines (LS SVM) used for the quality monitoring of medicated leaven fermentation. First, near infrared spectra of 67 medicated leaven samples were obtained by near infrared spectroscopy system in the wavelength range of 400 2 500 nm, and then the protease and amylase activity were measured by Folin phenol method and DNS method. Thereafter, the LS SVM was employed to calibrate models. The Rc and Rp of protease in near infrared model were 0.975 and 0.938, respectively; The RMSEC and RMSEP were 5.297 and 9.795, respectively. The Rc and Rp of amylase in near infrared model were 0.987 and 0.973, respectively; The RMSEC and RMSEP were 7.215 and 6.864, respectively. This model has good prediction ability and is suitable for quality monitoring in medicated leaven fermentation process. The research achievement could lay a certain foundation for the near infrared spectral analysis technology applied in the field of traditional fermentation processing.

OBJECTIVETo investigate the impact of repeated data acquisition on the stability of NIR quantitative calibration model, and make a preliminary analysis on reasons for the impact.

METHODYinhuang decoction was used as the subject, and NIR spectrum samples were collected. By reference to HPLC's determination value, the baicalin quantitative calibration model was established by using recursive least square algorithm to detect cumulative-LVs curve of latent variables. The impact of calibration model caused by repetitive samples was explained in latent variance space.

RESULTAfter averaging the repetitive spectrum samples, quantitative prediction model, which was built by optimal method of spectrum pretreatment, showed the ideal prediction result (RMSECV = 1.824). The area under the cumulative-LVs curve of latent variables was obviously larger than other modeling methods, i. e., this model is more stable.

CONCLUSIONAveraging of multiple measurements can dramatically improve the predictive ability of the model and make the model more stable.

Calibration ; Drugs, Chinese Herbal ; chemistry ; Models, Statistical ; Reproducibility of Results ; Spectrophotometry, Infrared ; methods ; Time Factors