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复杂体系中多组分定量分析的进展与预测:实用液相色谱 - 紫外检测法

Progress and prediction of multicomponent quantification in complex systems with practical LC-UV methods.

作者信息

Chen Xi, Yang Zhao, Xu Yang, Liu Zhe, Liu Yanfang, Dai Yuntao, Chen Shilin

机构信息

Institute of Chinese Materia Medica, China Academy of Chinese Medical Sciences, Beijing, 100700, China.

Key Lab of Separation Science for Analytical Chemistry, Dalian Institute of Chemical Physics, Chinese Academy of Sciences, Dalian, 116023, China.

出版信息

J Pharm Anal. 2023 Feb;13(2):142-155. doi: 10.1016/j.jpha.2022.11.011. Epub 2022 Dec 5.

Abstract

Complex systems exist widely, including medicines from natural products, functional foods, and biological samples. The biological activity of complex systems is often the result of the synergistic effect of multiple components. In the quality evaluation of complex samples, multicomponent quantitative analysis (MCQA) is usually needed. To overcome the difficulty in obtaining standard products, scholars have proposed achieving MCQA through the "single standard to determine multiple components (SSDMC)" approach. This method has been used in the determination of multicomponent content in natural source drugs and the analysis of impurities in chemical drugs and has been included in the Chinese Pharmacopoeia. Depending on a convenient (ultra) high-performance liquid chromatography method, how can the repeatability and robustness of the MCQA method be improved? How can the chromatography conditions be optimized to improve the number of quantitative components? How can computer software technology be introduced to improve the efficiency of multicomponent analysis (MCA)? These are the key problems that remain to be solved in practical MCQA. First, this review article summarizes the calculation methods of relative correction factors in the SSDMC approach in the past five years, as well as the method robustness and accuracy evaluation. Second, it also summarizes methods to improve peak capacity and quantitative accuracy in MCA, including column selection and two-dimensional chromatographic analysis technology. Finally, computer software technologies for predicting chromatographic conditions and analytical parameters are introduced, which provides an idea for intelligent method development in MCA. This paper aims to provide methodological ideas for the improvement of complex system analysis, especially MCQA.

摘要

复杂体系广泛存在,包括天然产物药物、功能性食品和生物样品等。复杂体系的生物活性往往是多种成分协同作用的结果。在复杂样品的质量评价中,通常需要进行多成分定量分析(MCQA)。为克服获取标准品的困难,学者们提出通过“一测多评”(SSDMC)方法实现MCQA。该方法已用于天然药物多成分含量测定及化学药物杂质分析,并已被《中国药典》收录。基于便捷的(超)高效液相色谱法,如何提高MCQA方法的重复性和稳健性?如何优化色谱条件以增加定量成分数量?如何引入计算机软件技术提高多成分分析(MCA)效率?这些是实际MCQA中有待解决的关键问题。首先,本文综述了近五年“一测多评”方法中相对校正因子的计算方法,以及方法的稳健性和准确性评价。其次,还总结了提高MCA峰容量和定量准确性的方法,包括色谱柱选择和二维色谱分析技术。最后,介绍了预测色谱条件和分析参数的计算机软件技术,为MCA的智能方法开发提供思路。本文旨在为复杂体系分析尤其是MCQA的改进提供方法学思路。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6347/9999300/688c4ddd4970/ga1.jpg

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