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Sensors 2014, 14(10), 18837-18850; doi:10.3390/s141018837

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

1
Department of Biosystems Machinery Engineering, College of Agricultural and Life Science, Chungnam National University, 99 Daehak-ro, Yuseong-gu, Daejeon 305-764, Korea
2
Environmental Microbiology and Food Safety Laboratory, Agricultural Research Service, U.S. Department of Agriculture, Powder Mill Rd. Bldg. 303, BARC-East, Beltsville, MD 20705, USA
3
Department of Food and Nutrition, Hanyang University, Seoul 133-791, Korea
4
Fruit Quality Laboratory, USDA-ARS, Beltsville, MD 20705, USA
*
Author to whom correspondence should be addressed.
Received: 7 August 2014 / Revised: 10 September 2014 / Accepted: 24 September 2014 / Published: 10 October 2014
(This article belongs to the Special Issue Agriculture and Forestry: Sensors, Technologies and Procedures)
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Abstract

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. View Full-Text
Keywords: hyperspectral near infrared reflectance imaging technique; crack tomato; imaging processing; principle component analysis; F-value hyperspectral near infrared reflectance imaging technique; crack tomato; imaging processing; principle component analysis; F-value
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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MDPI and ACS Style

Lee, H.; Kim, M.S.; Jeong, D.; Delwiche, S.R.; Chao, K.; Cho, B.-K. Detection of Cracks on Tomatoes Using a Hyperspectral Near-Infrared Reflectance Imaging System. Sensors 2014, 14, 18837-18850.

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