Development of a Metabolite Ratio Rule-Based Method for Automated Metabolite Profiling and Species Differentiation of Four Major Cinnamon Species

A metabolomic ratio rule-based classification method was developed and programmed for automated metaboliteprofiling and differentiation of four major cinnamon species using ultra-high-performance liquid chromatography-high-resolutionmass spectrometry (UHPLC-HRMS). The computational program identifies key cinnamon metabolites, includingproanthocyanidins, cinnamaldehyde, and coumarin, from test samples through LC-MS data processing and assigns cinnamonspecies by critical metabolite ratios using a stepwise classification strategy. Further, 100% classification accuracy was achieved on thetraining sample set through critical ratio optimization, and over 95% accuracy was achieved on the validation sample set. Theproposed cinnamon classification method exhibited superior accuracy compared to the metabolomic-based PLS-DA modelingmethod and offered great value for the authentication of cinnamon samples and evaluation of their potential health benefits