In silico production of relative correction factor for the quantitative analysis of multi‐components by single marker method. Issue 2 (30th July 2019)

• Record Type: Journal Article
• Authors: Yin, Ying‐Hao; Zheng, Jia‐Yi; Wang, Xin‐Nan; Qian, Sheng‐Ying; Liu, Xu‐Yi; Liu, Li‐Fang; Xin, Gui‐Zhong
• Other Names: Li Hui Jun guestEditor.; Yu Hua guestEditor.
• Is Part Of: Phytochemical analysis. Volume 32:Issue 2(2021)
• Page Start: 124
• Page End: 128
• Publication Date: 2019-07-30
• Subjects: high throughput -- In silico approach -- quantitative analysis of multi‐components single marker method -- relative correction factors -- traditional Chinese medicine; Plants -- Analysis -- Periodicals; Plants -- chemistry -- Periodicals; 572.2
• DOI: 10.1002/pca.2881
• Languages: English
• ISSNs: 0958-0344

Abstract

Abstract: Introduction: Traditional methods to derive experimentally‐generated relative correction factors (RCFs) for the quantitative analysis of herbal multi‐components by single marker (QAMS) method require reference standards and multiple validations with different instruments and columns, which hampers high throughput implementation. Objectives: To effectively reduce the application amounts of raw material and provide higher and more stable accuracy, this study aimed to develop a method to computationally generate RCFs of herbal components. Materials and Methods: This strategy included the published data collection, calibration curves screening, computer algorithm‐based RCFs generation and accuracy validation. Results: Using the in silico approach, we have successfully produced 133 RCFs for the multi‐component quantitative analysis of 63 widely used herbs. Conclusion: Compared with conventional RCFs, this in silico method would be a low cost and highly efficient way to produce practical RCFs for the QAMS method. Abstract : Here, an in‐silico based method was developed and successfully applied for the production of RCFs of 63 widely used herbs, which not only solved the problems associated with the absence of rare standard substances but also saved resources to improve work efficiency. This in‐silico method was of great practicability for its high throughput, high accuracy and low cost.