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Detection of cracks on tomatoes using a hyperspectral near-infrared reflectance imaging system.
Sensors (Basel). 2014 Oct 10; 14(10):18837-50.S

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.

Authors+Show Affiliations

Department of Biosystems Machinery Engineering, College of Agricultural and Life Science, Chungnam National University, 99 Daehak-ro, Yuseong-gu, Daejeon 305-764, Korea. Hoonsoolee83@gmail.com.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. moon.kim@ars.usda.gov.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. jeongdanhee@gmail.com.Fruit Quality Laboratory, USDA-ARS, Beltsville, MD 20705, USA. stephen.delwiche@ars.usda.gov.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. kevin.chao@ars.usda.gov.Department of Biosystems Machinery Engineering, College of Agricultural and Life Science, Chungnam National University, 99 Daehak-ro, Yuseong-gu, Daejeon 305-764, Korea. chobk@cnu.ac.kr.

Pub Type(s)

Journal Article
Research Support, Non-U.S. Gov't

Language

eng

PubMed ID

25310472

Citation

Lee, Hoonsoo, et al. "Detection of Cracks On Tomatoes Using a Hyperspectral Near-infrared Reflectance Imaging System." Sensors (Basel, Switzerland), vol. 14, no. 10, 2014, pp. 18837-50.
Lee H, Kim MS, Jeong D, et al. Detection of cracks on tomatoes using a hyperspectral near-infrared reflectance imaging system. Sensors (Basel). 2014;14(10):18837-50.
Lee, H., Kim, M. S., Jeong, D., Delwiche, S. R., Chao, K., & Cho, B. K. (2014). Detection of cracks on tomatoes using a hyperspectral near-infrared reflectance imaging system. Sensors (Basel, Switzerland), 14(10), 18837-50. https://doi.org/10.3390/s141018837
Lee H, et al. Detection of Cracks On Tomatoes Using a Hyperspectral Near-infrared Reflectance Imaging System. Sensors (Basel). 2014 Oct 10;14(10):18837-50. PubMed PMID: 25310472.
* Article titles in AMA citation format should be in sentence-case
TY - JOUR T1 - Detection of cracks on tomatoes using a hyperspectral near-infrared reflectance imaging system. AU - Lee,Hoonsoo, AU - Kim,Moon S, AU - Jeong,Danhee, AU - Delwiche,Stephen R, AU - Chao,Kuanglin, AU - Cho,Byoung-Kwan, Y1 - 2014/10/10/ PY - 2014/08/07/received PY - 2014/09/10/revised PY - 2014/09/24/accepted PY - 2014/10/14/entrez PY - 2014/10/14/pubmed PY - 2015/6/30/medline SP - 18837 EP - 50 JF - Sensors (Basel, Switzerland) JO - Sensors (Basel) VL - 14 IS - 10 N2 - 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. SN - 1424-8220 UR - https://www.unboundmedicine.com/medline/citation/25310472/Detection_of_cracks_on_tomatoes_using_a_hyperspectral_near_infrared_reflectance_imaging_system_ L2 - https://www.mdpi.com/resolver?pii=s141018837 DB - PRIME DP - Unbound Medicine ER -