Machine Learning Combining with Ultra‐High Performance Liquid Chromatography‐Time‐of‐Flight Mass Spectrometry and Gas Chromatography‐Ion Mobility Chromatography Data Fusion for Geographical Origins Discrimination of Lonicerae japonicae Flos

The objective was to achieve accurate origins discriminations of flos of Lonicerae japonicae, especially those from the genuine and non-genuine producing areas. An integration strategy was proposed based on machine learning, combining ultra-high performance liquid chromatography-time-of-flight mass spectrometry and gas chromatography-ion mobility chromatography data fusion for geographical origins discrimination of flos of Lonicerae japonicae. Sixty-one batches of flos of Lonicerae japonicae samples from different origins were determined by ultra-high performance liquid chromatography-time-of-flight mass spectrometry and gas chromatography-ion mobility chromatography. Multivariate statistical analysis, including principal component analysis and partial least-squares discriminant analysis were performed for the classification with an accuracy of 80.33% and 96.72%, respectively. Variable importance in projection (VIP>1) screened 9 nonvolatile differential constituents and 32 volatile differential constituents, respectively. The results of machine learning models combined with ultra-high performance liquid chromatography-time-of-flight mass spectrometry and gas chromatography-ion mobility chromatography data fusion indicated that the multilayer perceptron, logistic, gradient boosting decision tree, and Decision Tree (CART)) combined with a middle-level data fusion approach, achieve 100% classification accuracy with the training set. Among them, the multilayer perceptron algorithm achieved 100% accuracy for both the training and testing sets. These findings provide methodology support for tracing the origin of flos of Lonicerae japonicae.