Artificial intelligence in food allergen detection and prediction: advances, methodologies, and challenges

Food allergies affect over 220 million individuals worldwide and present increasing challenges due to complex food matrices and processing-induced protein modifications. Conventional detection methods, including immunoassays, PCR, and mass spectrometry, provide reliable analytical tools but are often limited by matrix interference, cross-reactivity, and labor-intensive workflows. Artificial intelligence (AI) has emerged as a complementary strategy, enabling high-throughput allergen prediction and enhanced analytical signal interpretation. This review examines recent advances in AI-driven allergen research across computational prediction and analytical detection. Machine learning (ML) and deep learning (DL) models achieve predictive accuracies exceeding 90% in sequence-based allergenicity assessment, outperforming traditional similarity-based methods. In analytical systems, AI-assisted spectroscopy and imaging enable rapid detection within seconds to minutes. Despite these advances, challenges remain in dataset bias, model interpretability, and cross-domain generalization. Future work should focus on explainable AI, standardized datasets, and external validation to support reliable and deployable allergen risk management systems. The integration of AI with spectroscopy, imaging, biosensing, and mass spectrometry is also highlighted.