Comparison of three officinal species of Callicarpa based on a biochemome profiling strategy with UHPLC-IT-MS and chemometrics analysis.

Traditional Chinese medicine (TCM) materials with closely related species are frequently fungible in clinical use. Therefore, holistic comparison of the composition in bioactive compounds is essential to evaluate whether they are equivalent in efficacy. Taking three officinal species of Callicarpa as a case, we proposed and validated a standardized strategy for the discrimination of closely related TCM materials, which focused on the extraction, profiling and multivariate statistical analysis of their biochemome. Firstly, serial liquid-liquid extractions were utilized to prepare different batches of Callicarpa biochemome, and the preparation yields were utilized for the normalization of sampling quantity prior to UHPLC-IT-MS analysis. Secondly, 34 compounds, including 19 phenylethanoid glycosides, 10 flavonoids and 5 terpenoids, were identified based on an untargeted UHPLC-IT-MS method. Thirdly, method validation of linearity, precision and stability showed that the UHPLC-IT-MS system was qualified (R2>0.995, RSD<15%) for subsequent biochemome profiling. After PCA and PLS-DA analysis, 30 marker compounds were screened and demonstrated to be of good predictability using genetic algorithm optimized support vector machines. Finally, a heatmap visualization was employed for clarifying the distribution of marker compounds, which could be helpful to determine whether the three Callicarpa species are, in fact, equivalent substitutes. This study provides a standardized biochemome profiling strategy for systemic comparison analysis of closely related TCM materials, which shows promising perspectives in tracking the supply chain of pharmaceutical suppliers.

A strategy to identify and quantify closely related adulterant herbal materials by mass spectrometry-based partial least squares regression.

In this study, a new strategy combining mass spectrometric (MS) techniques with partial least squares regression (PLSR) was proposed to identify and quantify closely related adulterant herbal materials. This strategy involved preparation of adulterated samples, data acquisition and establishment of PLSR model. The approach was accurate, sensitive, durable and universal, and validation of the model was done by detecting the presence of Fritillaria Ussuriensis Bulbus in the adulteration of the bulbs of Fritillaria unibracteata. Herein, three different MS techniques, namely wooden-tip electrospray ionization mass spectrometry (wooden-tip ESI/MS), ultra-performance liquid chromatography quadrupole time-of-flight mass spectrometry (UPLC-QTOF/MS) and UPLC-triple quadrupole tandem mass spectrometry (UPLC-TQ/MS), were applied to obtain MS profiles for establishing PLSR models. All three models afforded good linearity and good accuracy of prediction, with correlation coefficient of prediction (rp2) of 0.9072, 0.9922 and 0.9904, respectively, and root mean square error of prediction (RMSEP) of 0.1004, 0.0290 and 0.0323, respectively. Thus, this strategy is very promising in tracking the supply chain of herb-based pharmaceutical industry, especially for identifying adulteration of medicinal materials from their closely related herbal species.