HPLC Profile‐Based Chemometric Discrimination of <scp> <i>Cinnamomum cassia</i> </scp> by Geographic Origin

Cinnamon is a valuable commodity, used in medicine and many other fields, and its quality depends on its origin. Therefore, classifying cinnamon by its growing region is essential. This study classified 134 cinnamon samples by their geographical origin based on the content of three key compounds, coumarin, cinnamic acid, and cinnamaldehyde, using machine learning techniques, including XGBoost, support vector machine, random forest, voting, stacking, and convolutional neural network models. The results indicated that the XGBoost model achieved the highest accuracy (76.47%) among the targeted approaches. It also showed that using only the concentrations of these three was insufficient to reliably distinguish cinnamon groups. To address limitations, an untargeted approach used the full HPLC chromatogram at 280 nm. This approach significantly improved classification performance, with voting and stacking models reaching an accuracy of 94.18%. SHapley Additive exPlanations analysis further revealed the retention times corresponding to the most critical signals to model predictions. On the other hand, applying principal component analysis to reduce the data dimensionality significantly improved the performance of the established model to an impressive accuracy of 100%. These results suggested an efficient approach to discriminate cinnamon origins by integrating HPLC data with machine learning.