Supervised classification of bruised apples with respect to the time after bruising on the basis of hyperspectral imaging data |
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| Affiliation: | 1. Beijing Research Center of Intelligent Equipment for Agriculture, Beijing Academy of Agriculture and Forestry Sciences, Beijing 100097, China;2. State Key Laboratory of Mechanical System and Vibration, Shanghai Jiao Tong University, Shanghai 200240, China;1. Department of Biosystems Machinery Engineering, Chungnam National University, Daejeon 305-764, South Korea;2. Environmental Microbial and Food Safety Laboratory, Agricultural Research Service, USDA, Beltsville, MD 20705, USA;3. Food Quality Laboratory, Agricultural Research Service, USDA, Beltsville, MD 20705, USA;4. School of Biotechnology, Yeungnam University, Gyeongsan 712-749, South Korea;1. KU Leuven – MeBioS,Kasteelpark Arenberg 30, 3001 Heverlee, Belgium;2. Flanders Center of Postharvest Technology, Willem de Croylaan 42, 3001 Leuven, Belgium;1. Key Laboratory of Advanced Process Control for Light Industry (Ministry of Education), Jiangnan University, Wuxi 214122, PR China;2. U.S. Department of Agriculture, Agricultural Research Service, Sugarbeet and Bean Research Unit, 524 S. Shaw Lane, Room 224, Michigan State University, East Lansing, MI 48824, USA;3. Department of Biosystems and Agricultural Engineering, 524 S. Shaw Lane, Michigan State University, East Lansing, MI 48824, USA |
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| Abstract: | Apple bruising, as a mechanical damage, occurs due to impact, compression, vibration or abrasion during handling. However, the symptoms of this damage, browning and softening of the tissue, appear not immediately but after a certain period of time after bruising. For sorting and grading systems, the information about how long the bruise exists in affected fruit can be valuable. VNIR (visible and near-infrared) and SWIR (short wavelength infrared) spectral characteristics of sound and bruised apple tissues were analyzed during a two week period after bruising. Supervised classification methods, including support vector machines, linear logistic regression, neural networks and decision trees, were used and compared to check their effectiveness for distinguishing time after bruising with respect to five varieties of apples. The detection system included hyperspectral cameras equipped with sensors working in the visible and near-infrared (400–1000 nm) and short wavelength infrared (1000–2500 nm) ranges. The results of supervised classification revealed good applicability of hyperspectral imaging in VNIR and SWIR spectral ranges for detecting the number of days after bruising. The linear logistic regression neural networks models were found to be the best classifiers in the majority of models developed. Prediction accuracies higher than 90% were obtained for classification models on spectral data pretreated with the second derivative. |
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| Keywords: | Apple bruising Time after bruising Hyperspectral imaging Supervised classification |
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