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In this study, a fast and effective high-performance liquid chromatography method was developed to obtain a fingerprint chromatogram and quantitative analysis simultaneously of four indexes including gallic acid, chlorogenic acid, albiflorin and paeoniflorin of the traditional Chinese medicine Moluodan Concentrated Pill. The method was performed by using a Waters X-bridge C reversed phase column on an Agilent 1200S high-performance liquid chromatography system coupled with diode array detection. The mobile phase of the high-performance liquid chromatography method was composed of 20 mmol/L phosphate solution and acetonitrile with a 1 mL/min eluent velocity, under a detection temperature of 30°C and a UV detection wavelength of 254 nm. After the methodology validation, 16 batches of Moluodan Concentrated Pill were analyzed by this high-performance liquid chromatography method and both qualitative and quantitative evaluation results were achieved by similarity analysis, principal component analysis and hierarchical cluster analysis. The results of these three chemometrics were in good agreement and all indicated that batch 10 and batch 16 showed significant differences with the other 14 batches. This suggested that the developed high-performance liquid chromatography method could be applied in the quality evaluation of Moluodan Concentrated Pill.

Ginkgo leaves are widely utilised in Chinese herbal medicines and functional food additives. However, the quality of ginkgo leaves fluctuates obviously due to the variety of geographical environments and climate conditions. Real time release testing (RTRT) combined with near infrared (NIR) spectroscopy was used to improve the quality control of ginkgo leaves. The RTRT of ginkgo leaves was achieved by qualitative and quantitative analysis using NIR spectroscopy and acceptable releasing criteria. Partial least squares regression models were developed for quantitative analysis of flavonol glycoside (FG), moisture and extract contents in ginkgo leaves. The coefficients of determination for leave-one-out cross-validation in calibration were 0.93, 0.92 and 0.89 for FG, moisture and extract contents, respectively, and relative standard errors of prediction were 9.01%, 6.67% and 3.22%, respectively. A discriminant analysis model was developed for qualitative analysis of ginkgo leaves. The Mahalanobis distance values were used as the qualitative releasing criteria of RTRT based on the discriminant analysis. In addition, FG content ≥0.7%, moisture content ≤12% and extract content ≥25% were used as the quantitative releasing criteria of RTRT according to the Chinese Pharmacopoeia. The accuracy of RTRT for ginkgo leaves was 86.7% according to qualitative and quantitative analyses based on NIR spectra. The results obtained in this work demonstrated that RTRT combined with NIR spectroscopy is a powerful tool for the quality control of ginkgo leaves.

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