Faculty of Pharmaceutical Sciences, Center for Research and Development of Herbal Health Products, Khon Kaen University, Khon Kaen, 40002, Thailand.
J Nat Med. 2013 Jul;67(3):452-9. doi: 10.1007/s11418-012-0698-z. Epub 2012 Aug 25.
Near-infrared spectroscopy (NIR) was applied to the quantitative analysis of the concentration of alpha-mangostin (aM) in mangosteen pericarp powder (MP). The predicted results from the partial least squares chemometric method of various pretreatment data were compared to obtain the best calibration model. Two different types of containers (transparent capsules and glass vials) filled with the same samples were measured. For MP mixture in vials, the calibration model involving nine principal components (PC) could predict the amount of aM most accurately based on non-pretreatment spectral data. For MP mixture in capsules, the calibration model involving nine PC could predict the amount of aM most accurately based on first-derivative pretreatment spectra. The relationships of the calibration models for both samples had sufficiently linear plots. The standard error of cross-validation for the MP mixture in vials was lower and the R(2) values of validation were higher compared to the MP mixture in capsules. The equation for prediction of the concentration of aM in MP mixtures in vials is y = 0.9775x + 0.0425 with R(2) = 0.9950 and for those in capsules is y = 1.0264x + 0.0126 with R(2) = 0.9898. Both validation results indicated that the concentrations of aM in MP mixtures were predicted with sufficient accuracy and repeatability. NIR can be a useful tool for the quality control of herbal medicine in powder form without any sample preparation. The type and the shape of the container should be considered to obtain more accurate data.
近红外光谱(NIR)被应用于定量分析山竹果皮粉中α-倒捻子素(aM)的浓度。通过比较各种预处理数据的偏最小二乘化学计量学方法的预测结果,获得了最佳的校准模型。测量了两种不同类型的容器(透明胶囊和玻璃小瓶)中装有相同样品的光谱。对于小瓶中的 MP 混合物,基于非预处理光谱数据,涉及九个主成分(PC)的校准模型可以最准确地预测 aM 的含量。对于胶囊中的 MP 混合物,涉及九个 PC 的校准模型可以最准确地预测基于一阶导数预处理光谱的 aM 含量。两个样品的校准模型之间的关系具有足够的线性图。与胶囊中的 MP 混合物相比,小瓶中 MP 混合物的交叉验证标准误差较低,验证的 R(2)值较高。小瓶中 MP 混合物浓度预测的方程为 y = 0.9775x + 0.0425,R(2)= 0.9950,而胶囊中 MP 混合物的方程为 y = 1.0264x + 0.0126,R(2)= 0.9898。两种验证结果均表明,MP 混合物中 aM 的浓度可以以足够的准确性和可重复性进行预测。NIR 可以成为粉末状草药质量控制的有用工具,无需任何样品制备。应考虑容器的类型和形状,以获得更准确的数据。