Comparison of X-ray CT and MRI of watercore disorder of different apple cultivars |
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| Institution: | 1. BIOSYST-MeBioS, University of Leuven, Willem de Croylaan 42, 3001 Leuven, Belgium;2. LPF-TAGRALIA, Technical University of Madrid (UPM)-CEI MONCLOA, ETSI Agrónomos, Avda. Complutense s/n, 28040 Madrid, Spain;3. Flanders Centre of Postharvest Technology, Willem de Croylaan 42, 3001 Leuven, Belgium;4. Estación Experimental de AulaDei (CSIC), Avda. Montañana 1005-50059, Zaragoza, Spain;5. CAI of Nuclear Magnetic Resonance and Electronic Spin, Universidad Complutense de Madrid, CEI MONCLOA, Avda. Juan XXIII, 1, 28040 Madrid, Spain;6. Bruker microCT, Kartuizersweg 3B, 2550 Kontich, Belgium;7. VSG, Visualisation Sciences Group, Office 3, Impasse Rudolf Diesel, BP 50227, Mérignac Cedex 300708, France;8. Department of Metallurgy and Materials Engineering, University of Leuven, Kasteelpark Arenberg 44, 3001 Leuven, Belgium;1. BIOSYST-MeBioS, KU Leuven, Willem de Croylaan 42, Heverlee, Belgium;2. Centre for Postharvest and Refrigeration Research, Massey University, Palmerston North, New Zealand;3. Fonterra Research and Development Centre, Palmerston North, New Zealand;4. Zespri™ International Ltd., Mt Maunganui, New Zealand;1. Department of Biosystems and Agricultural Engineering, Michigan State University, East Lansing, MI 48824, USA;2. United States Department of Agriculture, Agricultural Research Service, Michigan State University, East Lansing, MI 48824, USA;1. Department of Biosystems and Agricultural Engineering, Michigan State University, 524 S. Shaw Lane, East Lansing, MI 48824, USA;2. Department of Plant, Soil and Microbial Sciences, Michigan State University, 612 Wilson Road, Room 158, East Lansing, MI 48824, USA;3. Department of Small Animal Clinical Sciences, D211 Veterinary Medical Center, Michigan State University, East Lansing, MI 48824, USA;1. Department of Biological Systems Engineering, Washington State University, Pullman, WA, USA;2. Center for Precision and Automated Agricultural Systems, Department of Biological Systems Engineering, IAREC, Washington State University, Prosser, WA, USA;1. The University of Queensland, School of Agriculture & Food Sciences, Gatton, Queensland 4343, Australia;2. Department of Agriculture, Fisheries and Forestry, EcoSciences Precinct, PO Box 267, Brisbane, Queensland 4560, Australia;3. The University of Queensland, Centre for Advanced Imaging, St. Lucia, Queensland 4067, Australia;4. Department of Agriculture, Fisheries and Forestry, PO Box 5083, SCMG Nambour, Queensland 4560, Australia |
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| Abstract: | Watercore is an internal disorder that appears as water-soaked, glassy regions near the core in apples. Fast and non-destructive solutions for sensing watercore would be readily accepted in the postharvest industry. X-ray CT and MRI were compared as potential imaging technologies for detecting this particular disorder. After matching the 3D datasets of X-ray CT and MRI, the images that were obtained on identical fruit were compared quantitatively. Both MRI and CT were able to detect watercore, however the contrast in MRI images was superior. High-resolution micro-CT images showed the microstructural changes in watercore fruit: the intercellular spaces of the affected apple tissue are filled with water. This explained the higher density that is detected here by the X-rays and the higher water content in the MRI. Mean and variance of the frequency distribution of MRI and X-ray CT intensity appeared to be a parameter that allows the identification of healthy apples from affected fruit. Automatic image processing based on thresholding the images resulted in comparable watercore classification accuracy of up to 89% for X-ray CT and 79% for MRI data, despite the better contrast in the MRI images. |
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| Keywords: | Automatic segmentation Histogram analysis Microstructure MRI X-ray CT Watercore disorder |
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