Comprehensive Analysis of Bile Medicines Based on UHPLC-QTOF-MS^E and Machine Learning.

ABSTRACT: Based on UHPLC-QTOF-MS^E analysis and quantized processing, combined with machine learning algorithms, data modeling was carried out to realize digital identification of bear bile powder (BBP), chicken bile powder (CIBP), duck bile powder (DBP), cow bile powder (CBP), sheep bile powder (SBP), pig bile powder (PBP), snake bile powder (SNBP), rabbit bile powder (RBP), and goose bile powder (GBP). First, 173 batches of bile samples were analyzed by UHPLC-QTOF-MS^E to obtain the retention time-exact mass (RTEM) data pair to identify bile acid-like chemical components. Then, the data were modeled by combining support vector machine (SVM), random forest (RF), artificial neural network (ANN), gradient boosting (GB), AdaBoost (AB), and Naive Bayes (NB), and the models were eval

SUBMITTER: Wang XR 

PROVIDER: S-EPMC11500153 | biostudies-literature | 2024 Oct

REPOSITORIES: biostudies-literature