Machine learning integration of multi-modal analytical data for distinguishing abnormal botanical drugs and its application in Guhong injection.

ABSTRACT:

Background

Determination of batch-to-batch consistency of botanical drugs (BDs) has long been the bottleneck in quality evaluation primarily due to the chemical diversity inherent in BDs. This diversity presents an obstacle to achieving comprehensive standardization for BDs. Basically, a single detection mode likely leads to substandard analysis results as different classes of structures always possess distinct physicochemical properties. Whereas representing a workaround for multi-target standardization using multi-modal data, data processing for information from diverse sources is of great importance for the accuracy of classification.

Methods

In this research, multi-modal data of 78 batches of Guhong injections (GHIs) consisting of 52 normal and 26 abnormal samples were a

SUBMITTER: Han Z 

PROVIDER: S-EPMC10759515 | biostudies-literature | 2024 Jan

REPOSITORIES: biostudies-literature