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

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

doi:10.1016/j.jpha.2022.11.011

Volume 13 Issue 2

Mar. 2023

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Article Navigation > Journal of Pharmaceutical Analysis > 2023 > 13(2): 142-155

Xi Chen, Zhao Yang, Yang Xu, Zhe Liu, Yanfang Liu, Yuntao Dai, Shilin Chen. Progress and prediction of multicomponent quantification in complex systems with practical LC-UV methods[J]. Journal of Pharmaceutical Analysis, 2023, 13(2): 142-155. doi: 10.1016/j.jpha.2022.11.011

Citation:

Xi Chen, Zhao Yang, Yang Xu, Zhe Liu, Yanfang Liu, Yuntao Dai, Shilin Chen. Progress and prediction of multicomponent quantification in complex systems with practical LC-UV methods[J]. Journal of Pharmaceutical Analysis, 2023, 13(2): 142-155. doi: 10.1016/j.jpha.2022.11.011

Citation:

Xi Chen, Zhao Yang, Yang Xu, Zhe Liu, Yanfang Liu, Yuntao Dai, Shilin Chen. Progress and prediction of multicomponent quantification in complex systems with practical LC-UV methods[J]. Journal of Pharmaceutical Analysis, 2023, 13(2): 142-155. doi: 10.1016/j.jpha.2022.11.011

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

doi: 10.1016/j.jpha.2022.11.011

Xi Chen^a,
Zhao Yang^a,
Yang Xu^b,
Zhe Liu^b,
Yanfang Liu^b,
Yuntao Dai^{a
,
,},
Shilin Chen^{a,c
,
,}

a. Institute of Chinese Materia Medica, China Academy of Chinese Medical Sciences, Beijing, 100700, China;
b. Key Lab of Separation Science for Analytical Chemistry, Dalian Institute of Chemical Physics, Chinese Academy of Sciences, Dalian, 116023, China;
c. Institute of Herbgenomics, Chengdu University of Traditional Chinese Medicine, Chengdu, 611137, China

Funds:

T Major Special Project for New Innovative Drugs Sponsored (Grant No.: 2019ZX09201005).

The authors thank the support by the National Natural Science Foundation of China (Grant No.: 81803734) and National S&

Received Date: Sep. 05, 2022
Accepted Date: Nov. 28, 2022
Rev Recd Date: Nov. 24, 2022
Publish Date: Mar. 07, 2023

Abstract

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.
- Multicomponent quantification analysis,
- Single standard to determine multiple components,
- Predictive software

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References(138)

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