Enhancing the detection of LTP through lyophilized protein samples and NIR spectroscopy with explainable deep learning.

Lipid transfer proteins (LTPs) are clinically relevant allergens widely present in plant-based foods, and their reliable detection in complex food matrices remains a major challenge. In this study, we developed an integrated framework combining near-infrared spectroscopy (NIRS), deep learning, and explainability methods to enable accurate and interpretable identification of LTPs. A total of 11,688 spectral measurements were collect ed using a FLAME-NIR spectrometer (940-1700 nm) from homogenized food samples, purified Pru p 3 and Ara h 9 proteins, and their mixtures with LTP-free matrices such as yogurt and powdered milk. Spectral preprocessing involved first derivative transformation, Standard Normal Variate correction, and feature scaling, followed by dimensionality reduction through a 1D convolutional autoencoder, which generated 64-dimensional latent embeddings. These representations were used to train two deep learning classifiers Convolutional Neural Networks (CNNs) and TabTransformer optimized via Bayesian optimization. The inclusion of purified protein embeddings substantially improved classification performance. The CNN model achieved the highest performance with 95.8% accuracy, 97.3% precision, 96.9% F1-score, and an AUC-ROC of 0.954, outperforming the TabTransformer, which nonetheless reached 95.19% accuracy and 96.4% F1-score. Model explainability was addressed using SHAP and LIME, which identified key latent features corresponding to specific spectral regions (940-1700 nm) associated with allergenic signatures. Compared to baseline models, the protein-enhanced framework demonstrated marked improvements in specificity and overall robustness. These results highlight the value of incorporating purified protein information into AI-based spectral analysis, offering a portable, non-destructive, and interpretable strategy for allergen detection in food safety applications.