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Detection of deoxynivalenol, its modified forms, and zearalenone in individual oat grains using visible-near-infrared spectroscopy and near-infrared hyperspectral imaging

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2026-03-09

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0956-7135

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Teixido-Orries I, Molino F, Verheecke-Vaessen C, et al., (2026) Detection of deoxynivalenol, its modified forms, and zearalenone in individual oat grains using visible-near-infrared spectroscopy and near-infrared hyperspectral imaging. Food Control, Volume 184, June 2026, Article number 112033

Abstract

Fusarium mycotoxins such as deoxynivalenol (DON), its modified forms, and zearalenone (ZEN) frequently contaminate oats, posing serious health and regulatory concerns. This study assessed the use of visible-near-infrared (Vis-NIR) spectroscopy and near-infrared hyperspectral imaging (NIR-HSI) to classify individual oat grains according to the European legal limits for DON (1750 μg/kg) and ZEN (100 μg/kg). NIR-HSI consistently outperformed Vis-NIR, achieving classification accuracies (CAs) above 91% and F1-scores above 0.65 for DON, ZEN and combined DON + ZEN detection. The most informative spectral regions were in the NIR ranges of 1000-1250 nm and 1300-1500 nm, associated with Fusarium-induced biochemical and structural changes in oat grains. Reducing the spectral input to 20 selected wavelengths preserved NIR-HSI performance, supporting the feasibility of multispectral implementations. These targeted, non-destructive approaches could enable early removal of the few highly contaminated grains responsible for batch rejection, improving food safety, reducing waste, and enhancing the sustainability of oat processing.

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Applied mycology, Deoxynivalenol, Modified forms, Zearalenone, Near-infrared, Visible range, Spectroscopy, 30 Agricultural, Veterinary and Food Sciences, 40 Engineering, Food Science, 3006 Food sciences, 4004 Chemical engineering

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Attribution-NonCommercial 4.0 International

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This work was supported by the Spanish Ministry of Science and Innovation (predoctoral grant FPU21/00073 and Project PID2020-114836RB-I00 funded by MCIN/AEI/10.13039/501100011033) and Cranfield University.

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