RESUMO
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.