Novel artificial intelligence approach for nsLTP early detection using NIRs data
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Fecha
2025-07-29
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Springer
Resumen
Food 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.
Palabras clave
Allergens
Artificial intelligence
Classification
Near-infrared spectroscopy
Artificial intelligence
Classification
Near-infrared spectroscopy
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Materias
Cita
Rodriguez-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/S12161-025-02851-6