Rapid determination of nine components in the first extraction process of Xingnaojing injection by using ultraviolet spectroscopy / 中国中药杂志

In this study, an analytical method based on ultraviolet spectroscopy was established for the rapid determination of nine components including isophorone, 4-methylene-isophorone, curcumenone, curcumenol, curdione, curzerenone, furanodienone, curcumol and germacrone in the first extraction process of Xingnaojing injection. 166 distillate samples of Gardeniae Fructus and Radix Curcumae were collected in the first extraction process of Xingnaojing injection. The ultraviolet spectra of these samples were collected, and the contents of the nine components in these samples were determined by high performance liquid chromatography. Least squares support vector machine and radial basis function artificial neural network were used to establish the multivariate calibration models between the ultraviolet spectra and the contents of the nine components. The results showed that the established ultraviolet spectrum analysis method can determine the contents of the nine components in the distillates accurately, with root mean square error of prediction of 0.068, 0.147, 0.215, 0.319, 1.01, 1.27, 0.764, 0.147, 0.610 mg•L⁻¹, respectively. This proposed method is a rapid, simple and low-cost tool for the monitoring and endpoint determination of the extraction process of Xingnaojing injection to reduce quality defects and variations.

Study on the Applicability of NIR Combined with LS-SVM Used for the Quality Monitoring of Medicated Leaven Fermentation / 世界科学技术-中医药现代化

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.