Rodríguez Alonso, AlexBarrio, Itxasne delBernardo Seisdedos, GanekoOsa Sánchez, AinhoaGarcía-Zapirain, Begoña2026-03-122026-03-122025-07-29Rodriguez-Alonso, A., Del Barrio, I., Bernardo-Seisdedos, G., Osa-Sanchez, A., & Garcia-Zapirain, B. (2025). Novel artificial intelligence approach for nsLTP early detection using NIRs data. Food Analytical Methods, 18(10), 2331-2343. https://doi.org/10.1007/S12161-025-02851-61936-975110.1007/S12161-025-02851-6https://hdl.handle.net/20.500.14454/5426Food allergies have become a significant public health issue, particularly lipid transfer protein (LTP) allergies, which are highly stable allergens and can cause severe allergic reactions. This research aims to develop and validate an AI-driven solution for detecting LTPs in food using near-infrared spectroscopy (NIRS), exploring the feasibility of non-invasive allergen identification using AI-assisted spectroscopy. The methodology involves collecting spectral data from various food samples, building a machine learning model, and optimizing it iteratively to improve detection accuracy. The results show that the AI model achieved an accuracy of 87% and an F1-score of 89.91%, indicating its potential for enhancing food safety. In conclusion, this solution demonstrates the viability of using NIRS and AI for allergen detection, with promising future applications in healthcare.eng© The Author(s) 2025AllergensArtificial intelligenceClassificationNear-infrared spectroscopyjournal article2026-03-121936-976X