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Assessing guava maturity by statistical analyses of dropped fruit impact responses
Institution:1. Department of Biomechatronic Engineering, National Chiayi University, Chiayi, Taiwan;2. Department of Mechanical and Energy Engineering, National Chiayi University, Chiayi, Taiwan;1. Department of Plant Sciences, University of California, Davis, One Shields Avenue, Davis, CA, 95616, USA;2. Department of Statistics, University of California, Davis, One Shields Avenue, Davis, CA, 95616, USA;1. Department of Biomechanics, Hospital for Special Surgery, New York, NY 10021, United States;2. Tissue Engineering, Regeneration and Repair Program, Hospital for Special Surgery, New York, NY 10021, United States;3. Department of Statistics, The Ohio State University, Columbus, OH 43210, United States;4. Department of Radiology and Imaging, Hospital for Special Surgery, New York, NY 10021, United States;5. Department of Biomedical Engineering, University of Rochester, Rochester, NY 14627, United States;1. Department of Neurological Surgery, Mayo Clinic Florida, Jacksonville, Florida, USA;2. Department of Neurological Surgery, University of Florida College of Medicine, Jacksonville, Florida, USA;1. Applied Ion Beam Physics Laboratory, Institute of Modern Physics, Fudan University, Shanghai 200433, China;2. Center for High Pressure Science & Technology Advanced Research, Shanghai 202103, China;3. State Key Laboratory of ASIC & System, Fudan University, Shanghai 200433, China
Abstract:A non-destructive method for assessing the maturity of guava fruit was developed based on the mechanical properties obtained from dropped fruit impact responses. The levels of maturity were classified with cluster and discriminant analyses on the raw impact measurements and their derived indices. The number of indices being processed was reduced with stepwise regression analysis. The accuracy of classification was improved using linear discriminant analysis to 76.3% with the penetrometer stiffness as a calibrator and to 84.2% with postharvest days as a calibrator. The performance shows that falling impact together with adequate statistical analyses provides a promising non-destructive approach in assessing the maturity of guava. The non-destructive nature was validated by repeating the test on the same specimen. The test mechanism is mechanical and can therefore be integrated as the maturity inference engine on an automated guava sorter.
Keywords:Falling impact  Guava  Fruit maturity  Statistical analysis  Storage day
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