Combining NMR and Artificial Intelligence to Improve Food Identification: Insights from a Pilot Project with the Central Inspectorate of Quality Protection and Fraud Repression (ICQRF, part of the Italian Ministry of Agriculture
a Politecnico di Bari, DICATECh – via Orabona 4, CAMPUS, I – 70125, Bari, Italy
b Innovative Solutions S.r.l. – Zona H 150/B, 70015, Noci (BA), Italy
c Ministero dell’agricoltura, della sovranità alimentare e delle foreste, Ispettorato centrale della tutela della qualità e repressione frodi dei prodotti agroalimentari, Ufficio PREF IV – Via Quintino Sella, 42, 00187, Roma, Italy
Food safety and fraud prevention are of great importance in maintaining public health, safeguarding consumer trust, and ensuring the integrity of the import/export fluxes. The increasing complexity and globalization of the food industry have made it increasingly challenging to detect and combat food fraud effectively. This study investigates the potential of integrating Nuclear Magnetic Resonance (NMR) spectroscopy and Artificial Intelligence (AI) techniques to address these challenges and enhance food safety and fraud detection capabilities. NMR spectroscopy is a powerful analytical tool capable of identifying and quantifying metabolites in complex food matrices, providing detailed information on food composition and quality. AI techniques, can process vast amounts of data efficiently, identifying patterns and making predictions with high accuracy.
In collaboration with the Central Inspectorate of Quality Protection and Fraud Repression (ICQRF), a pilot program was conducted to evaluate the effectiveness of this integrated approach. The project focused on two food products with a high potential for fraud: lentils and wheat. These products were selected due to their economic significance and vulnerability to adulteration and mislabeling. A set of authentic and non-authentic samples were provided by ICQRF and analyzed using NMR spectroscopy. The resulting spectral data were then processed using AI algorithms.
The integrated NMR and AI approach demonstrated a high level of accuracy in identifying solid information about the origin of the samples, cultivar, and agronomical practices used. Additionally, the methodology offered meaningful insights into the samples’ attributes, emphasizing details related to seasonal effects and the geographic origin of the fields in which they were grown. This contributed to a more thorough comprehension of the samples’ history and growth conditions.
The findings from this pilot program suggest that the integration of NMR and AI can significantly enhance the capabilities of food safety authorities to detect and prevent food fraud, is also important to consistently update the samples database, ensuring that the classification model remains effective in recognizing new samples with every changing season.