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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...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2014
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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. |
format | Online Article Text |
id | pubmed-4239932 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2014 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
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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