Classification and quantification of sesame oil in edible oils and adulterated mixtures using ¹H NMR spectroscopy...

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.