Pseudotargeted metabolomics-based random forest model for tracking plant species from herbal products

Background: The "one-to-multiple" phenomenon is prevalent in medicinal herbs. Accurate species identification is critical to ensure the safety and efficacy of herbal products but is extremely challenging due to their complex matrices and diverse compositions. Purpose: This study aimed to identify the determinable chemicalome of herbs and develop a reasonable strategy to track their relevant species from herbal products. Methods: Take Astragali Radix-the typical "one to multiple" herb, as a case. An in-house database-driven identification of the potentially bioactive chemicalome (saponins and flavonoids) in AR was performed. Furthermore, a pseudotargeted metabolomics method was first developed and validated to obtain high-quality semi-quantitative data. Then based on the data matrix, the random forest algorithm was trained to predict Astragali Radix species from commercial products. Results: The pseudotargeted metabolomics method was first developed and validated to obtain high-quality semiquantitative data (including 56 saponins and 49 flavonoids) from 26 batches of AR. Then the random forest algorithm was well-trained by importing the valid data matrix and showed high performance in predicting Astragalus species from ten commercial products. Conclusion: This strategy could learn species-special combination features for accurate herbal species tracing and could be expected to promote the traceability of herbal materials in herbal products, contributing to manufacturing standardization.