Progress and prediction of multicomponent quantification in complex systems with practical LC-UV methods
作者机构：Institute of Chinese Materia MedicaChina Academy of Chinese Medical SciencesBeijing100700China Key Lab of Separation Science for Analytical ChemistryDalian Institute of Chemical PhysicsChinese Academy of SciencesDalian116023China Institute of HerbgenomicsChengdu University of Traditional Chinese MedicineChengdu611137China
出 版 物：《Journal of Pharmaceutical Analysis》 (药物分析学报（英文版）)
年 卷 期：2023年第13卷第2期
页 面：142-155页
核心收录：
学科分类：1007[医学-药学(可授医学、理学学位)] 10[医学]
基 金：the National Natural Science Foundation of China(Grant No.:81803734) National S&T Major Special Project for New Innovative Drugs Sponsored(Grant No.:2019ZX09201005)
主 题：Multicomponent quantification analysis Single standard to determine multiple components Predictive software
摘 要：Complex systems exist widely,including medicines from natural products,functional foods,and biological *** biological activity of complex systems is often the result of the synergistic effect of multiple *** the quality evaluation of complex samples,multicomponent quantitative analysis(MCQA)is usually *** overcome the difficulty in obtaining standard products,scholars have proposed achieving MCQA through the“single standard to determine multiple components(SSDMC)*** 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 *** 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 ***,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 ***,it also summarizes methods to improve peak capacity and quantitative accuracy in MCA,including column selection and twodimensional chromatographic analysis ***,computer software technologies for predicting chromatographic conditions and analytical parameters are introduced,which provides an idea for intelligent method development in *** paper aims to provide methodological ideas for the improvement of complex system analysis,especially MCQA.
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