title: Optimal fluorescence waveband determination for detecting defective cherry tomatoes using a fluorescence excitation-emission matrix.
abstract: A multi-spectral fluorescence imaging technique was used to detect defective cherry tomatoes. The fluorescence excitation and emission matrix was used to measure for defects, sound surface and stem areas to determine the optimal fluorescence excitation and emission wavelengths for discrimination. Two-way ANOVA revealed the optimal excitation wavelength for detecting defect areas was 410 nm. Principal component analysis (PCA) was applied to the fluorescence emission spectra of all regions at 410 nm excitation to determine the emission wavelengths for defect detection. The major emission wavelengths were 688 nm and 506 nm for the detection. Fluorescence images combined with the determined emission wavebands demonstrated the feasibility of detecting defective cherry tomatoes with >98% accuracy. Multi-spectral fluorescence imaging has potential utility in non-destructive quality sorting of cherry tomatoes.
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. huntersp@naver.com.
authors.forename: In-Suck
authors.initials: IS
authors.lastname: Baek
authors.affiliation: Environmental Microbiology and Food Safety Laboratory, Agricultural Research Service, U.S. Department of Agriculture, Powder Mill Road, Building #303, BARC-East, Beltsville, MD 20705, USA. moon.kim@ars.usda.gov.
authors.forename: Moon S
authors.initials: MS
authors.lastname: Kim
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. benet33@naver.com.
authors.forename: Hoosoo
authors.initials: H
authors.lastname: Lee
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. wanghee@cnu.ac.kr.
authors.forename: Wang-Hee
authors.initials: WH
authors.lastname: Lee
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: D000465:Algorithms; D005504:Food Analysis / Q000379:methods*; D005638:Fruit / Q000033:anatomy & histology* / Q000145:classification*; D007090:Image Interpretation, Computer-Assisted / Q000379:methods; D018551:Solanum lycopersicum / Q000033:anatomy & histology* / Q000145:classification*; D008856:Microscopy, Fluorescence / Q000379:methods*; D056910:Republic of Korea; D013050:Spectrometry, Fluorescence / Q000379:methods*