Detection of LTP in Food Using Near-Infrared Spectroscopy and Explainable Artificial Intelligence

A food allergy is a sensitivity to a food or one of its components that triggers the immune system to react. It affects around 8% of children and 10% of adults. This study looks into the possibilities of near-infrared spectroscopy mixed with artificial intelligence approaches for quick and non-destructive detection of LTP, a severe food allergy. NIRS spectra were acquired from a wide variety of foods, both with and without LTP, using a miniature spectrometer. Three deep learning architectures (convolutional neural networks, vision transformers, and TabTransformer) were employed to classify foods according to the presence of LTP, optimizing hyperparameters using Bayesian optimization. The findings indicated that ViT and CNN-based models had significant potential, with accuracies of more than 90% in both accuracy and F1. Key wavelengths in the 1325-1455 nm range were identified as useful for identifying foods with LTP, indicating that the models account for changes in water, fat, and protein content. Additionally, AI explainability methods (SHAP, LIME, and GradCAM) were used to better comprehend model decisions. The study's findings show the potential of NIRS and AI as useful tools for enhancing food safety through rapid and dependable allergy detection.