Simultaneous determination of four active compounds in Centella asiatica by supramolecular solvent-based extraction coupled with high performance liquid chromatography-tandem mass spectrometry.

In the present study, a new and rapid method for determining four bioactive compounds of Centella asiatica (C. asiatica) in herbs was developed based on high performance liquid chromatography coupled with tandem mass spectrometry (HPLC-MS/MS). Supramolecular solvent (SUPRAS), formed by n-hexanol, tetrahydrofuran (THF) and water, was used for extracting madecassoside (MS), asiaticoside (AS), asiatic acid (AA) and madecassic acid (MA) from herbs. The sample was extracted with 4 mL of SUPRAS for 5 min. Then centrifugation was performed for phase separation followed by direct analysis by HPLC-MS/MS. Driving forces for the extraction of herbs in the SUPRAS involved both dispersion and hydrogen bond interactions. The effect of the parameters, including compounds of supramolecular solvents, dosage and vortex time, on the extraction efficiency was investigated. The recoveries were carried out at three levels with spiked samples and in the range of 91.6%-99.9%, with relative standard deviations between 1.7%-7.9%. The novel SUPRAS method, coupled with HPLC-MS/MS, was proved to be efficiency, green, and sensitive. It was applied for determination of four target compounds in herbs.

Polycephalums A and B, New Anticoagulant Constituents Isolated from Clinopodium polycephalum.

Two previously undescribed compounds (1 and 2) were isolated from Clinopodium polycephalum, a medicinal plant distributed in southwestern and eastern China. Their structures were elucidated using MS analyses and extensive 2D-homo and heteronuclear NMR data interpretations. Both compounds 1 and 2 could significantly shorten APTT and PT, and their procoagulant effect was comparable to that of positive drugs. At the same time, compound 2 had certain antioxidant activity (IC50 value of 2.25±0.05 µM in ABTS assay).

Fingerprint analysis of phenolic acid extract of Salvia miltiorrhiza by digital reference standard analyzer with one or two reference standards.

BACKGROUND: Fingerprint analysis and simultaneous multi-components determination are crucial for the holistic quality control of traditional Chinese medicines (TCMs). Yet, reference standards (RS) are often commercially unavailable and with other shortages, which severely impede the application of these technologies. METHODS: A digital reference standard (DRS) strategy and the corresponding software called DRS analyzer, which supports chromatographic algorithms, spectrum algorithms, and the combination of these algorithms, was developed. The extensive function also enabled the DRS analyzer to recommend the chromatographic column based on big data. RESULTS: Various quality control methods of fingerprints of 11 compounds in polyphenolic acid extract of Salvia miltiorrhiza (S. miltiorrhiza) were developed based on DRS analyzer, involving relative retention time (RRT) method, linear calibration using two reference substances (LCTRS) technique, RRT combined with Photon Diode Array (PDA) method, LCTRS combined with PDA method. Additionally, the column database of samples was established. Finally, our data demonstrated that the DRS analyzer could accurately identify 11 compounds of the samples, using only one or two physical RSs. CONCLUSIONS: The DRS strategy is an automated, intelligent, objective, accurate, eco-friendly, universal, sharing, and promising method for overall quality control of TCMs that requires the usage of fewer RSs.

Application of Chemometric Algorithms in the High-Performance Thin-Layer Chromatography Fingerprint of Traditional Chinese Medicines.

Background: The use of HPTLC fingerprinting for the analysis of traditional Chinese medicines (TCMs) usually involves several image-processing steps. However, these image-processing steps are time consuming. Objective: We describe a new approach that applies artificial neural networks (ANN) directly to raw high-performance thin-layer chromatography HPTLC images. Methods: This approach combines image processing and chemometric modeling and was used to classify TCMs [dried tangerine eel (Chen Pi), green tangerine peel (Qing Pi), immature bitter orange fruit, and bitter orange fruit (Zhi Qiao)]. Images of the plates were processed with Chempattern and chemometric analysis including PCA, PLS-DA, and kNN were carried out all by ChemPattern. Results: The ANN model has an accuracy of 100.00% in all training, validation, and test sets, indicating excellent predictive performance and good generalization ability. The k-nearest neighbors (kNN) and partial least-square discriminant analysis (PLS-DA) models have accuracies of 90.91 and 72.73%, respectively, with the independent test set. The kNN model is also accurate, simple, and can be easily interpreted. Conclusions: HPTLC fingerprinting, combined with advanced image processing and proper chemometric algorithms, is a simple, efficient, and accurate method for the analysis of TCMs. Highlights: HPTLC fingerprints of four TCM crude drugs derived from Citrus spp. were compared by using image analysis algorithms. A new approach that applied ANN directly to raw HPTLC fingerprint images was described. Three image analysis algorithms based on kNN, PLS-DA and ANN are compared in the paper. The ANN model shows excellent predictive performance with high accuracy in test sets.