Haibin Qu

Development and Validation of a Method for the Determination of Tanshinones in Supercritical Fluid Extraction Products by HPLC

Abstract Supercritical fluid extraction has been merging as a promising alternative technique for the extraction of the lipid soluble compounds from natural resources for the nutrient food. However, to evaluate SFE process and its products, it is usually only by measuring one of the main constituents or the extract yield, which sometimes would be thought as defective and unreasonable. In this article, we presented a multi‐active component analysis method to evaluate the SFE of Salvia miltiorrhiza. A reverse‐phase HPLC method for the simultaneous analysis of the four tanshinones (dihydrotanshinone I, cryptotanshinone, tanshinone I and tanshinone IIA) from the SFE products was developed with a phase composition as water/acetonitrile. The gradient elution of the mobile phase was 60% (acetonitrile) in 0–17 min, 60–80% in 17–25 min, and 80% in 25–30 min. By this comprehensive evaluation method, the SFE process of Salvia miltiorrhiza was investigated and variables involved in the procedure such as pressure, temperature, and extraction time were optimized.

Kinetic modeling for chromatographic separation of cytosine monophosphate and uracil monophosphate

Kinetic modeling for preparative chromatography is a topic of present interest in the fine chemicals and pharmaceutical industries. In this study, chromatographic separation of the two nucleotides CMP and UMP was simulated by the equilibrium-dispersive (ED) model, and the adsorption isotherms in the ED model were determined by the inverse method. Prediction performance of the model was validated under three different kinds of conditions and the importance of selecting isotherms was discussed in detail. Excellent agreement was achieved with the experimental band profiles and the prediction of the ED model. The ED model with bi-Langmuir isotherm was especially suitable for simulating chromatographic separation of CMP and UMP. The error of prediction by the ED model with bi-Langmuir isotherm was about 9.4 times smaller than that with Langmuir isotherm.