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

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 t...

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Autores principales: López-Maestresalas, Ainara, Lopez-Molina, Carlos, Oliva-Lobo, Gil Alfonso, Jarén, Carmen, Ruiz de Galarreta, Jose Ignacio, Peraza-Alemán, Carlos Miguel, Arazuri, Silvia
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9618585/
https://www.ncbi.nlm.nih.gov/pubmed/36324619
http://dx.doi.org/10.3389/fnut.2022.999877
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author López-Maestresalas, Ainara
Lopez-Molina, Carlos
Oliva-Lobo, Gil Alfonso
Jarén, Carmen
Ruiz de Galarreta, Jose Ignacio
Peraza-Alemán, Carlos Miguel
Arazuri, Silvia
author_facet López-Maestresalas, Ainara
Lopez-Molina, Carlos
Oliva-Lobo, Gil Alfonso
Jarén, Carmen
Ruiz de Galarreta, Jose Ignacio
Peraza-Alemán, Carlos Miguel
Arazuri, Silvia
author_sort López-Maestresalas, Ainara
collection PubMed
description 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.
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spelling pubmed-96185852022-11-01 Evaluation of near-infrared hyperspectral imaging for the assessment of potato processing aptitude López-Maestresalas, Ainara Lopez-Molina, Carlos Oliva-Lobo, Gil Alfonso Jarén, Carmen Ruiz de Galarreta, Jose Ignacio Peraza-Alemán, Carlos Miguel Arazuri, Silvia Front Nutr Nutrition 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. Frontiers Media S.A. 2022-10-17 /pmc/articles/PMC9618585/ /pubmed/36324619 http://dx.doi.org/10.3389/fnut.2022.999877 Text en Copyright © 2022 López-Maestresalas, Lopez-Molina, Oliva-Lobo, Jarén, Ruiz de Galarreta, Peraza-Alemán and Arazuri. https://creativecommons.org/licenses/by/4.0/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.
spellingShingle Nutrition
López-Maestresalas, Ainara
Lopez-Molina, Carlos
Oliva-Lobo, Gil Alfonso
Jarén, Carmen
Ruiz de Galarreta, Jose Ignacio
Peraza-Alemán, Carlos Miguel
Arazuri, Silvia
Evaluation of near-infrared hyperspectral imaging for the assessment of potato processing aptitude
title Evaluation of near-infrared hyperspectral imaging for the assessment of potato processing aptitude
title_full Evaluation of near-infrared hyperspectral imaging for the assessment of potato processing aptitude
title_fullStr Evaluation of near-infrared hyperspectral imaging for the assessment of potato processing aptitude
title_full_unstemmed Evaluation of near-infrared hyperspectral imaging for the assessment of potato processing aptitude
title_short Evaluation of near-infrared hyperspectral imaging for the assessment of potato processing aptitude
title_sort evaluation of near-infrared hyperspectral imaging for the assessment of potato processing aptitude
topic Nutrition
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9618585/
https://www.ncbi.nlm.nih.gov/pubmed/36324619
http://dx.doi.org/10.3389/fnut.2022.999877
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