Evaluation of near-infrared hyperspectral imaging for the assessment of potato processing aptitude

López Maestresalas, Ainara; López Molina, Carlos; Oliva Lobo, Gil Alfonso; Jarén Ceballos, Carmen; Ruiz de Galarreta, José Ignacio; Peraza Alemán, Carlos Miguel; Arazuri Garín, Silvia

Publication:
Evaluation of near-infrared hyperspectral imaging for the assessment of potato processing aptitude

dc.contributor.author	López Maestresalas, Ainara
dc.contributor.author	López Molina, Carlos
dc.contributor.author	Oliva Lobo, Gil Alfonso
dc.contributor.author	Jarén Ceballos, Carmen
dc.contributor.author	Ruiz de Galarreta, José Ignacio
dc.contributor.author	Peraza Alemán, Carlos Miguel
dc.contributor.author	Arazuri Garín, Silvia
dc.contributor.department	Ingeniaritza	eu
dc.contributor.department	Estatistika, Informatika eta Matematika	eu
dc.contributor.department	Institute on Innovation and Sustainable Development in Food Chain - ISFOOD	en
dc.contributor.department	Ingeniería	es_ES
dc.contributor.department	Estadística, Informática y Matemáticas	es_ES
dc.date.accessioned	2022-11-04T07:03:25Z
dc.date.available	2022-11-04T07:03:25Z
dc.date.issued	2022
dc.date.updated	2022-11-03T10:25:30Z
dc.description.abstract	The potato (Solanum tuberosum L.) is the world's fifth most important staple food with high socioeconomic relevance. Several potato cultivars obtained by selection and crossbreeding are currently on the market. This diversity causes tubers to exhibit different behaviors depending on the processing to which they are subjected. Therefore, it is interesting to identify cultivars with specific characteristics that best suit consumer preferences. In this work, we present a method to classify potatoes according to their cooking or frying as crisps aptitude using NIR hyperspectral imaging (HIS) combined with a Partial Least Squares Discriminant Analysis (PLS-DA). Two classification approaches were used in this study. First, a classification model using the mean spectra of a dataset composed of 80 tubers belonging to 10 different cultivars. Then, a pixel-wise classification using all the pixels of each sample of a small subset of samples comprised of 30 tubers. Hyperspectral images were acquired using fresh-cut potato slices as sample material placed on a mobile platform of a hyperspectral system in the NIR range from 900 to 1,700 nm. After image processing, PLS-DA models were built using different pre-processing combinations. Excellent accuracy rates were obtained for the models developed using the mean spectra of all samples with 90% of tubers correctly classified in the external dataset. Pixel-wise classification models achieved lower accuracy rates between 66.62 and 71.97% in the external validation datasets. Moreover, a forward interval PLS (iPLS) method was used to build pixel-wise PLS-DA models reaching accuracies above 80 and 71% in cross-validation and external validation datasets, respectively. Best classification result was obtained using a subset of 100 wavelengths (20 intervals) with 71.86% of pixels correctly classified in the validation dataset. Classification maps were generated showing that false negative pixels were mainly located at the edges of the fresh-cut slices while false positive were principally distributed at the central pith, which has singular characteristics.	en
dc.description.sponsorship	This work was funded by the Ministerio de Ciencia e Innovación (Spanish Ministry of Science and Innovation) (project PID2019-109790RR-C22).	en
dc.format.mimetype	application/pdf	en
dc.identifier.citation	López-Maestresalas, A., Lopez-Molina, C., Oliva-Lobo, G. A., Jarén, C., Ruiz de Galarreta, J. I., Peraza-Alemán, C. M., & Arazuri, S. (2022). Evaluation of near-infrared hyperspectral imaging for the assessment of potato processing aptitude. Frontiers in Nutrition, 9, 999877.	en
dc.identifier.doi	10.3389/fnut.2022.999877
dc.identifier.issn	2296-861X
dc.identifier.uri	https://academica-e.unavarra.es/handle/2454/44267
dc.language.iso	eng	en
dc.publisher	Frontiers Media	en
dc.relation.ispartof	Frontiers In Nutrition 9:999877	en
dc.relation.projectID	info:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2017-2020/PID2019-109790RR-C22/ES/	en
dc.relation.publisherversion	https://doi.org/10.3389/fnut.2022.999877
dc.rights	© 2022 López-Maestresalas, Lopez-Molina, Oliva-Lobo, Jarén, Ruiz de Galarreta, Peraza-Alemán and Arazuri. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.	en
dc.rights.accessRights	Acceso abierto / Sarbide irekia	es
dc.rights.accessRights	info:eu-repo/semantics/openAccess	en
dc.subject	Potato	en
dc.subject	NIR hyperspectral imaging (HIS)	en
dc.subject	Partial Least Squares Discriminant Analysis (PLS-DA)	en
dc.title	Evaluation of near-infrared hyperspectral imaging for the assessment of potato processing aptitude	en
dc.type	Artículo / Artikulua	es
dc.type	info:eu-repo/semantics/article	en
dc.type.version	Versión publicada / Argitaratu den bertsioa	es
dc.type.version	info:eu-repo/semantics/publishedVersion	en
dspace.entity.type	Publication
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