title: Detection of cracks on tomatoes using a hyperspectral near-infrared reflectance imaging system.
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.affiliation: 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.
authors.forename: Hoonsoo
authors.initials: H
authors.lastname: Lee
authors.affiliation: 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.
authors.forename: Moon S
authors.initials: MS
authors.lastname: Kim
authors.affiliation: 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.
authors.forename: Danhee
authors.initials: D
authors.lastname: Jeong
authors.affiliation: Fruit Quality Laboratory, USDA-ARS, Beltsville, MD 20705, USA. stephen.delwiche@ars.usda.gov.
authors.forename: Stephen R
authors.initials: SR
authors.lastname: Delwiche
authors.affiliation: 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.
authors.forename: Kuanglin
authors.initials: K
authors.lastname: Chao
authors.affiliation: 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.
authors.forename: Byoung-Kwan
authors.initials: BK
authors.lastname: Cho
mesh_terms: D016002:Discriminant Analysis; D006801:Humans; D018551:Solanum lycopersicum / Q000592:standards*; D019265:Spectroscopy, Near-Infrared / Q000379:methods*; D060388:Support Vector Machine