Evaluation and classification of dissolution behavior and capability of Chinese medicine granules based on an inline turbidity sensor / 中国中药杂志

In this paper, the inline turbidity sensor technology was used to quantify the turbidity of the solution during the dissolution of Chinese medicine granules. The probe measurement position and the magnetic stirring speed were optimized. As a result, the stirring speed was 400 r·min~(-1), and the probe position was at 1/4 of the diameter of the beaker. The measurement results were accurate and reliable. Totally 105 batches of commercially available Chinese medicine granules were collected and dissolved according to the requirements of the Chinese Pharmacopoeia. At the time point of 5 min, 57 batches of granules were completely dissolved, and the corresponding turbidity values ranged between 0-70 FTU; 32 batches of granules showed a slight turbidity, and the corresponding turbidity values ranged between 70-350 FTU; 14 batches of granule solution were turbid, and the corresponding turbidity values ranged between 350-2 000 FTU; two batches of granule solution were heavily turbid, and the corresponding turbidity values were >2 000 FTU. Among the above results, the number of batches in line with the pharmacopoeia dissolution requirement was 84.76%, and the dissolution of some granules still needed to be improved. The turbidity sensor recorded the change curve of turbidity value over time(solubility behavior curve). The degree of important of disintegration and dissolution during the dissolution process showed disintegration > dissolution, disintegration≈dissolution, disintegration < dissolution. The dissolution behavior of the granules can be classified into three categories. The analysis of the mechanism in the process of granule solubility provides a basis for product process improvement.

Near-infrared online monitoring and quality control of Salvianolate column separation / 中成药

AIM:To study the feasibility of online monitoring and controlling of the key process of column chromatographic separation in the production of Salvianolate by combining near-infrared(NIR) spectrum and HPLC fingerprinting. METHODS: Partial Least Square(PLS) regression was used to model the correlation of NIR spectra with the concentrations of salvianolic acid B,and the influences of light path,wavelength selection,and preprocessing method on the PLS model were investigated systematically. RESULTS: A 2 mm light path was better than 1 mm one,and with 2 mm light path,the optimal wave-number was in the range of 6 102.1-5 446.3 cm~(-1) and the optimal preprocessing method was the vector normalization.The root mean-square error of PLS model on test samples was 0.234 mg/mL,and R~2 was 0.995 2. CONCLUSION: This method is proved to be fast,convenient,and precise.It can be used to online monitoring and quality control of the manufacturing of Salvianolate.