Non-destructive detection of blackspot in potatoes by Vis-NIR and SWIR hyperspectral imaging
| dc.contributor.author | López Maestresalas, Ainara | |
| dc.contributor.author | Keresztes, Janos C. | |
| dc.contributor.author | Goodarzi, Mohammad | |
| dc.contributor.author | Arazuri Garín, Silvia | |
| dc.contributor.author | Jarén Ceballos, Carmen | |
| dc.contributor.author | Saeys, Wouter | |
| dc.contributor.department | Proyectos e Ingeniería Rural | es_ES |
| dc.contributor.department | Landa Ingeniaritza eta Proiektuak | eu |
| dc.contributor.funder | Universidad Pública de Navarra / Nafarroako Unibertsitate Publikoa | es |
| dc.date.accessioned | 2018-12-16T19:33:27Z | |
| dc.date.available | 2018-12-16T19:33:27Z | |
| dc.date.issued | 2016 | |
| dc.description.abstract | Blackspot is a subsurface potato damage resulting from impacts during harvesting. This type of bruising represents substantial economic losses every year. As the tubers do not show external symptoms, bruise detection in potatoes is not straightforward. Therefore, a nondestructive and accurate method capable of identifying bruised tubers is needed. Hyperspectral imaging (HSI) has been shown to be able to detect other subsurface defects such as bruises in apples. This method is nondestructive, fast and can be fully automated. Therefore, its potential for non-destructive detection of blackspot in potatoes has been investigated in this study. Two HSI setups were used, one ranging from 400 to 1000 nm, named VisibleNear Infrared (Vis-NIR) and another covering the 1000e2500 nm range, called Short Wave Infrared (SWIR). 188 samples belonging to 3 different varieties were divided in two groups. Bruises were manually induced and samples were analyzed 1, 5, 9 and 24 h after bruising. PCA, SIMCA and PLS-DA were used to build classifiers. The PLS-DA model performed better than SIMCA, achieving an overall correct classification rate above 94% for both hyperspectral setups. Furthermore, more accurate results were obtained with the SWIR setup at the tuber level (98.56 vs. 95.46% CC), allowing the identification of early bruises within 5 h after bruising. Moreover, the pixel based PLS- DA model achieved better results in the SWIR setup in terms of correctly classified samples (93.71 vs. 90.82% CC) suggesting that it is possible to detect blackspot areas in each potato tuber with high accuracy. | en |
| dc.description.sponsorship | The funding of this work has been covered by the Universidad Pública de Navarra through the concession of both a predoctoral research grant (Res. 1753/2012) and a mobility grant (Res. 1506/ 2013), by the National Institute for Agricultural and Food Research and Technology (INIA) project: “Mejora genetica de la patata: caracterizacion reol ogica y por tecnología NIRS del material” RTA2013-00006-C03-03, and by the Agency for Innovation by Science and Technology in Flanders (IWT) through the Chameleon (SB-100021) project. | en |
| dc.format.mimetype | application/pdf | en |
| dc.identifier.doi | 10.1016/j.foodcont.2016.06.001 | |
| dc.identifier.issn | 0956-7135 | |
| dc.identifier.uri | https://academica-e.unavarra.es/handle/2454/31829 | |
| dc.language.iso | eng | en |
| dc.publisher | Elsevier | en |
| dc.relation.ispartof | Food Control, 70 (2016) 229-241 | en |
| dc.relation.projectID | info:eu-repo/grantAgreement/MINECO//RTA2013-00006-C03-03/ES/ | |
| dc.relation.publisherversion | http://dx.doi.org/10.1016/j.foodcont.2016.06.001 | |
| dc.rights | © 2016 Elsevier Ltd. All rights reserved. The manuscript version is made available under the CC BY-NC-ND 4.0 license. | en |
| dc.rights.accessRights | info:eu-repo/semantics/openAccess | |
| dc.rights.uri | https://creativecommons.org/licenses/by-nc-nd/4.0/ | |
| dc.subject | Vis-NIR | en |
| dc.subject | SWIR | en |
| dc.subject | Hyperspectral imaging | en |
| dc.subject | Solanum tuberosum L. | en |
| dc.subject | Potato | en |
| dc.subject | Blackspot | en |
| dc.title | Non-destructive detection of blackspot in potatoes by Vis-NIR and SWIR hyperspectral imaging | en |
| dc.type | info:eu-repo/semantics/article | |
| dc.type.version | info:eu-repo/semantics/acceptedVersion | |
| dspace.entity.type | Publication | |
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