Department of Pharmacognosy, Faculty of Pharmacy, Ain Shams University, Cairo, Egypt.
Department of Pharmacognosy, Faculty of Pharmacy, University of Aden, Aden, Yemen.
Phytochem Anal. 2022 Mar;33(2):320-330. doi: 10.1002/pca.3089. Epub 2021 Oct 27.
Traditional herbal medicines are mostly composed of more than one herb which act synergistically; hence, there is high demand for proper quality control methods to ensure the consistent quality of polyherbal formulations.
Proposing a simple, reliable, and efficient method for the qualitative and quantitative analysis of a polyherbal product using multivariate analysis of ultraviolet-visible (UV-Vis) spectroscopy or HPLC-PDA data.
An antiobesity formula consisting of equal proportions of Trachyspermum ammi, Cuminum cyminum, and Origanum majorana was prepared as well as spiked with one of each herb simultaneously, representing accepted and unaccepted samples. Full factorial design (2 ) was applied to study the effect of temperature, sonication, and stirring time for extraction optimisation. The HPLC and UV spectral fingerprints were separately subjected to multivariate analysis. The soft independent modelling of class analogy (SIMCA) and partial least squares (PLS) models were developed to segregate the accepted from the unaccepted samples and to predict the herbal composition in addition to the thymol content in each sample.
The SIMCA and SIMCA models showed correct discrimination between the accepted and unaccepted samples with excellent selectivity and sensitivity. The PLS , PLS , and PLS models showed excellent linearity and accuracy with R > 0.98, slope close to 1, intercept close to 0, low root mean square error of calibration (RMSEC), and root mean square error of prediction (RMSEP) (close to 0). On validation, the PLS models correctly predicted the quantity of the three herbs and thymol content with ±5% accuracy.
This study demonstrates the reliability and efficiency of HPLC and UV spectroscopy coupled with multivariate statistical analysis (MVA) for ensuring the consistency of polyherbal preparations.
传统草药大多由多种草药组成，这些草药协同作用；因此，需要适当的质量控制方法来确保复方制剂的质量一致。
提出一种简单、可靠、高效的方法，用于使用多元分析紫外可见（UV-Vis）光谱或 HPLC-PDA 数据对复方产品进行定性和定量分析。
制备一种由等量的胡芦巴、孜然和牛至组成的减肥药方，并同时添加一种草药作为接受和不接受的样本。采用全因子设计（2 ）研究提取优化的温度、超声和搅拌时间的影响。分别对 HPLC 和 UV 光谱指纹进行多元分析。建立软独立建模分类类比（SIMCA）和偏最小二乘（PLS）模型，以区分接受和不接受的样本，并预测每个样本中的草药成分和百里香酚含量。
SIMCA 和 SIMCA 模型对接受和不接受的样本进行了正确的区分，具有出色的选择性和灵敏度。PLS、PLS 和 PLS 模型具有出色的线性和准确性，R ²>0.98，斜率接近 1，截距接近 0，校准的均方根误差（RMSEC）和预测的均方根误差（RMSEP）低（接近 0）。验证时，PLS 模型正确预测了三种草药和百里香酚含量的数量，准确率为 ±5%。
本研究证明了 HPLC 和 UV 光谱与多元统计分析（MVA）相结合用于确保复方制剂一致性的可靠性和效率。
