Decoding the aroma signature of Jinhua ham: Flavoromics-driven machine learning models for age-discrimination with...

Jinhua ham, a geographical indication agriculture product in China, possesses exquisite flavor during fermentation. Three-year Jinhua ham is highly valued for superior flavor and nutrition, but current age-discrimination relies on the experience of workers, leaving high adulteration risks. In this study, Gas Chromatography-Mass Spectrometry (GC-MS) and Gas Chromatography-Ion Mobility Spectrometry (GC-IMS) were adopted to analyze the differences of flavor compounds in Jinhua ham aged for 0.5, 1, 2 and 3 years. Fifty-four aroma-active compounds were identified through OAV ≥ 1 from Jinhua ham with different ages, with the flavor changing from mushroom to citrus/grassy/buttery, then nutty, and finally to dominant coconut, peach, and floral-fatty notes. And 18 key discriminative compounds were screened out through plotted compounds content distribution. Further, 1-octen-3-ol and decanoic acid were found applicable for discriminating the age of ham based on the scatter plot and SHAP value. Then, four age-discriminating machine-learning models (LR, SVM, NB, and DT) were constructed and exhibited high accuracy, with the highest reaching 100 %. Especially, a portable ham quality evaluation circuit was constructed based on the DT classification models due to its good generalization ability and concise structure. This study provides a theoretical basis for accurately predicting ages of Jinhua ham by GC-MS with machine learning.