Distinguishing the botanical origins of rare honey through untargeted metabolomics and machine learning interpreting flavonoid profiles

Distinguishing the botanic origins of monofloral honey is the foremost concern in ensuring its authentication. In this work, an innovative, green, and comprehensive approach was developed to distinguish the botanic origins of four types of rare honey, and the strategy involved in the following aspects: Based on theoretical design, suitable natural deep eutectic solvent (NADES) was screened to extract flavonoids from honey samples; after NADES extracts were directly analyzed by high-resolution mass spectrometry, the discrimination models of monofloral honey were established by untargeted metabolomics combined with machine learning. Based on the comparison of various models, the Random Forest algorithm had higher prediction accuracy for four types of monofloral honey, and characteristic compounds for each rare monofloral honey were screened based on SHapley Additive exPlanations values. This work provides a new perspective on the use of AI technology and green chemistry to control the quality of honey.