Non-targeted metabolomics and explainable artificial intelligence: Effects of processing and color on coniferyl aldehyde levels in Eucommiae cortex

Abstract

Eucommia ulmoides, a plant native to China, is valued for its medicinal properties and has applications in food, health products, and traditional Chinese medicine. Processed Eucommiae Cortex (EC) has historically been a highly valued medicine. Ancient doctors had ample experience processing EC, especially with ginger juice, as documented in traditional Chinese medical texts. The combination of EC and ginger juice helps release and transform the active ingredients, strengthening the medicine's effectiveness and improving its taste and shelf life. However, the lack of quality control standards for Ginger-Eucommiae Cortex (G-EC), processed from EC and ginger, presents challenges for its industrial and clinical use. This study optimized G-EC processing using the CRITIC and Box-Behnken methods. Metabolomics showed 517 chemical changes between raw and processed G-EC, particularly an increase in coniferyl aldehyde (CFA). Explainable artificial intelligence techniques revealed the feasibility of using color to CFA content, providing insights into quality indicators.

Keywords: Color recognition; Coniferyl aldehyde; Content prediction; Eucommiae cortex; Ginger-Eucommiae cortex; Molecular networking; Non-targeted metabolomics; SHapley additive exPlanations.