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| dc.creator | Femenias, Antoni | |
| dc.creator | Gatius Cortiella, Ferran | |
| dc.creator | Ramos Girona, Antonio J. | |
| dc.creator | Teixido-Orries, Irene | |
| dc.creator | Marín Sillué, Sònia | |
| dc.date | 2022 | |
| dc.date.accessioned | 2025-11-03T12:15:31Z | |
| dc.date.available | 2025-11-03T12:15:31Z | |
| dc.identifier | https://doi.org/10.1016/j.foodres.2022.111102 | |
| dc.identifier | 0963-9969 | |
| dc.identifier | http://hdl.handle.net/10459.1/83276 | |
| dc.identifier.uri | http://fima-docencia.ub.edu:8080/xmlui/handle/123456789/24140 | |
| dc.description | One of the most common concerns in the cereal industry is the presence of fungi and their associated mycotoxins. Hyperspectral Imaging (HSI) has been proposed recently as one of the most potent tools to manage fungal associated contamination. The introduction of a spatial dimension to the spectral analysis allows the selection of the specific regions of the sample for further screening. Single kernel analysis would enable the discrimination of the highly contaminated kernels to establish a mitigation strategy, overcoming the contamination heterogeneity of cereal batches. This document is a detailed review of the HSI recently published studies that aimed to discriminate fungi and mycotoxin contaminated single cereal kernels. The most relevant findings showed that fungal infection and mycotoxins levels discrimination accuracies were above 90% and 80%, respectively. The results indicate that NIR-HSI is suitable for the detection of fungal-related contamination in single kernels and it has potential to be applied at food industry stages. | |
| dc.description | This work was supported by Project AGL2017-87755-R funded by MCIN/ AEI /10.13039/501100011033/ FEDER ?Una manera de hacer Europa? and project PID2020-114836RB-I00 funded by MCIN/ AEI /10.13039/501100011033. The authors are grateful to the University of Lleida (predoctoral grant). | |
| dc.language | eng | |
| dc.publisher | Elsevier | |
| dc.relation | info:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2017-2020/PID2020-114836RB-I00/ES/ESTRATEGIAS DE MITIGACION DE LA CONTAMINACION POR DEOXINIVALENOL Y FUMONISINAS EN ALIMENTOS A BASE DE MAIZ Y AVENA/ | |
| dc.relation | info:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2013-2016/AGL2017-87755-R/ES/TECNICAS DE SELECCION Y PROCESADO DE CEREALES, Y SU IMPACTO EN LA CONTAMINACION POR DEOXINIVALENOL EN ALIMENTOS INFANTILES/Sa | |
| dc.relation | Reproducció del document publicat a https://doi.org/10.1016/j.foodres.2022.111102 | |
| dc.relation | Food Research International, 2022, vol. 155, núm. 111102, p. 1-12 | |
| dc.rights | cc-by (c) Femenias et. al., 2022 | |
| dc.rights | info:eu-repo/semantics/openAccess | |
| dc.rights | http://creativecommons.org/licenses/by/4.0/ | |
| dc.subject | Cereal sorting | |
| dc.subject | Mycotoxins | |
| dc.subject | Fungal infections | |
| dc.subject | Hyperspectral imaging | |
| dc.subject | Single-kernel analysis | |
| dc.subject | Fongs fitopatògens | |
| dc.subject | Fongs en l'agricultura | |
| dc.title | Hyperspectral imaging for the classification of individual cereal kernels according to fungal and mycotoxins contamination: A review | |
| dc.type | info:eu-repo/semantics/article | |
| dc.type | info:eu-repo/semantics/publishedVersion |
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