ScholarWorks@중앙대학교

초록

Sesame oil is often adulterated with cheaper oils, necessitating accurate authentication and quantification methods. This study investigates the performance of AI-based models using 1H NMR spectral data for edible oil classification and sesame oil quantification in adulterated mixtures. All classification models—PCA-LDA, SVM, and 1D-CNN—achieved 100 % accuracy, with 1D-CNN additionally capturing both lignan and fatty acid signals. For regression, PLSR and SVR models achieved RMSEP values of 1.94 and 1.40 (R2 = 0.998), while the 1D-CNN regression model demonstrated superior performance (RMSEP = 1.03, R2 = 0.999) with broader spectral feature integration. External test samples incorporating previously unused oil types further validated the robustness of the CNN model, which accurately predicted sesame oil content within a 2 % error margin. These findings highlight the potential of explainable deep learning integrated with NMR spectroscopy for reliable detection and quantification of adulterated sesame oil.

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