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Detection of Cracks on Tomatoes Using a Hyperspectral Near-Infrared Reflectance Imaging System

The objective of this study was to evaluate the use of hyperspectral near-infrared (NIR) reflectance imaging techniques for detecting cuticle cracks on tomatoes. A hyperspectral NIR reflectance imaging system that analyzed the spectral region of 1000–1700 nm was used to obtain hyperspectral reflecta...

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Autores principales: Lee, Hoonsoo, Kim, Moon S., Jeong, Danhee, Delwiche, Stephen R., Chao, Kuanglin, Cho, Byoung-Kwan
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2014
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4239932/
https://www.ncbi.nlm.nih.gov/pubmed/25310472
http://dx.doi.org/10.3390/s141018837
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author Lee, Hoonsoo
Kim, Moon S.
Jeong, Danhee
Delwiche, Stephen R.
Chao, Kuanglin
Cho, Byoung-Kwan
author_facet Lee, Hoonsoo
Kim, Moon S.
Jeong, Danhee
Delwiche, Stephen R.
Chao, Kuanglin
Cho, Byoung-Kwan
author_sort Lee, Hoonsoo
collection PubMed
description The objective of this study was to evaluate the use of hyperspectral near-infrared (NIR) reflectance imaging techniques for detecting cuticle cracks on tomatoes. A hyperspectral NIR reflectance imaging system that analyzed the spectral region of 1000–1700 nm was used to obtain hyperspectral reflectance images of 224 tomatoes: 112 with and 112 without cracks along the stem-scar region. The hyperspectral images were subjected to partial least square discriminant analysis (PLS-DA) to classify and detect cracks on the tomatoes. Two morphological features, roundness (R) and minimum-maximum distance (D), were calculated from the PLS-DA images to quantify the shape of the stem scar. Linear discriminant analysis (LDA) and a support vector machine (SVM) were then used to classify R and D. The results revealed 94.6% and 96.4% accuracy for classifications made using LDA and SVM, respectively, for tomatoes with and without crack defects. These data suggest that the hyperspectral near-infrared reflectance imaging system, in addition to traditional NIR spectroscopy-based methods, could potentially be used to detect crack defects on tomatoes and perform quality assessments.
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spelling pubmed-42399322014-11-21 Detection of Cracks on Tomatoes Using a Hyperspectral Near-Infrared Reflectance Imaging System Lee, Hoonsoo Kim, Moon S. Jeong, Danhee Delwiche, Stephen R. Chao, Kuanglin Cho, Byoung-Kwan Sensors (Basel) Article The objective of this study was to evaluate the use of hyperspectral near-infrared (NIR) reflectance imaging techniques for detecting cuticle cracks on tomatoes. A hyperspectral NIR reflectance imaging system that analyzed the spectral region of 1000–1700 nm was used to obtain hyperspectral reflectance images of 224 tomatoes: 112 with and 112 without cracks along the stem-scar region. The hyperspectral images were subjected to partial least square discriminant analysis (PLS-DA) to classify and detect cracks on the tomatoes. Two morphological features, roundness (R) and minimum-maximum distance (D), were calculated from the PLS-DA images to quantify the shape of the stem scar. Linear discriminant analysis (LDA) and a support vector machine (SVM) were then used to classify R and D. The results revealed 94.6% and 96.4% accuracy for classifications made using LDA and SVM, respectively, for tomatoes with and without crack defects. These data suggest that the hyperspectral near-infrared reflectance imaging system, in addition to traditional NIR spectroscopy-based methods, could potentially be used to detect crack defects on tomatoes and perform quality assessments. MDPI 2014-10-10 /pmc/articles/PMC4239932/ /pubmed/25310472 http://dx.doi.org/10.3390/s141018837 Text en © 2014 by the authors; licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution license (http://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Lee, Hoonsoo
Kim, Moon S.
Jeong, Danhee
Delwiche, Stephen R.
Chao, Kuanglin
Cho, Byoung-Kwan
Detection of Cracks on Tomatoes Using a Hyperspectral Near-Infrared Reflectance Imaging System
title Detection of Cracks on Tomatoes Using a Hyperspectral Near-Infrared Reflectance Imaging System
title_full Detection of Cracks on Tomatoes Using a Hyperspectral Near-Infrared Reflectance Imaging System
title_fullStr Detection of Cracks on Tomatoes Using a Hyperspectral Near-Infrared Reflectance Imaging System
title_full_unstemmed Detection of Cracks on Tomatoes Using a Hyperspectral Near-Infrared Reflectance Imaging System
title_short Detection of Cracks on Tomatoes Using a Hyperspectral Near-Infrared Reflectance Imaging System
title_sort detection of cracks on tomatoes using a hyperspectral near-infrared reflectance imaging system
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4239932/
https://www.ncbi.nlm.nih.gov/pubmed/25310472
http://dx.doi.org/10.3390/s141018837
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