Discrimination of omega‐3 fatty acid oil forms by combining NMR spectroscopy with artificial intelligence

This study presents an approach for discriminating omega‐3 fatty acid forms using proton nuclear magnetic resonance ( 1 H‐NMR) spectroscopy combined with machine learning and deep learning techniques. A total of 90 samples, comprising triglyceride, re‐esterified triglyceride, and ethyl ester forms, were analyzed. Principal component analysis–linear discriminant analysis, support vector machine (SVM), artificial neural network (ANN), and one‐dimensional convolutional neural network (1D CNN) models were applied using binned spectral data. In contrast, a two‐dimensional convolutional neural network (2D CNN) was constructed using spectral images. To prevent overfitting and optimize model hyperparameters, early stopping, cross‐validation, and Bayesian optimization were used across the different machine learning and deep learning models. The 1D and 2D CNN models both achieved 100% accuracy on the training and test sets, while the SVM and ANN models yielded slightly lower but still excellent performance, with a test accuracy of 94.4%. Model interpretability was enhanced through SHapley Additive exPlanations and Gradient‐weighted Class Activation Mapping, which identified critical spectral regions associated with classification decisions. These results demonstrate that the integration of artificial intelligence techniques with 1 H‐NMR spectroscopy enables accurate, interpretable discrimination of omega‐3 fatty acid forms, offering a promising strategy for supplement authentication and quality control.