Near infrared hyperspectral imaging as a sorting tool for deoxynivalenol reduction in wheat batches
2024
Sanchís Almenar, Vicente | Pascari, Xenia | Molino Gahete, Francisco | Ramos Girona, Antonio J. | Marín Sillué, Sònia
The present study aimed to evaluate the feasibility of using near-infrared hyperspectral imaging (NIR-HSI) and chemometrics for classification of individual wheat kernels according to their deoxynivalenol (DON) level. In total, 600 wheat kernels from samples naturally contaminated over the maximum EU level were collected, and the DON content in each individual wheat kernel was analyzed by UHPLC. Linear discriminant analysis (LDA) was employed for building classification models of DON using the EU maximum level as cut off level, and they were tested on balanced and imbalanced test sets. The results showed that the models presented a balanced accuracy of 0.71, that would allow to obtain safe batches from contaminated batches once the unsafe kernels had been rejected, but often more than 30% of the batch would be rejected. The work confirmed that NIR-HSI could be a feasible method for monitoring DON in individual kernels and removing highly contaminated kernels prior to food chain entry.
Afficher plus [+] Moins [-]This work was supported by the Spanish Ministry of Science and Innovation (Spain), Project PDC2021-121191-I00 funded by MCIN/AEI/10.13039/501100011033 and European Union Next Generation EU/PRTR and EU funded Project 101079173 -FunShield4Med (HORIZON-WIDERA-2021-ACCESS-03).
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