Hyperspectral laser-induced fluorescence imaging for assessing apple fruit quality |
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| Institution: | 1. Postharvest Technology Research Laboratory, South African Research Chair in Postharvest Technology, Department of Food Science, Stellenbosch University, Private Bag X1, Stellenbosch 7602, South Africa;2. Postharvest Technology Research Laboratory, South African Research Chair in Postharvest Technology, Department of Horticultural Science, Stellenbosch University, Private Bag X1, Stellenbosch 7602, South Africa;3. Department of Crop Science, School of Agricultural, Earth and Environmental Science, University of KwaZulu-Natal, Private Bag X01, Scottsville, 3209, Pietermaritzburg, South Africa;1. Centre for Optical and Electromagnetic Research, Zhejiang Provincial Key Laboratory for Sensing Technologies, State Key Laboratory Modern Optical Instrumentation, JORCEP [Joint Research Center of Photonics of the Royal Institute of Technology, Lund University and Zhejiang University], Zhejiang University, Hangzhou 310058, PR China;2. Philips (China) Investment Co., Ltd, NO. 10, Shanghai 200233, PR China;1. State Key Laboratory of Mechanical System and Vibration, Shanghai Jiao Tong University, Shanghai 200240, China;2. Beijing Research Center of Intelligent Equipment for Agriculture, Beijing 100097, China;3. National Research Center of Intelligent Equipment for Agriculture, Beijing 100097, China;4. Key Laboratory of Agri-informatics, Ministry of Agriculture, Beijing 100097, China;5. Beijing Key Laboratory of Intelligent Equipment Technology for Agriculture, Beijing 100097, China;1. U.S. Department of Agriculture, Agricultural Research Service, U.S. Agricultural Research Station, 1636 E. Alisal St, Salinas, CA 93905, USA;2. High Resolution Plant Phenomics Centre, Australian Plant Phenomics Facility, CSIRO Plant Industry, GPO Box 1600, Canberra, ACT 2601, Australia;1. National Institute of Agricultural Sciences, Rural Development Administration, 310 Nonsaengmyeong-ro, Wansan-gu, Jeonju-si, Jeollabuk-do 54875, Republic of Korea;2. Environmental Microbial and Food Safety Laboratory, BARC-East, Agricultural Research Service, US Department of Agriculture, 10300 Baltimore Avenue, Beltsville, MD 20705, United States;3. Food Quality Laboratory, Agricultural Research Service, US Department of Agriculture, 10300 Baltimore Avenue, Beltsville, MD 20705, United States;4. Department of Biosystems Machinery Engineering, College of Agricultural and Life Science, Chungnam National University, 99 Daehak-ro, Yuseong-gu, Daejeon 34134, Republic of Korea |
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| Abstract: | Chlorophyll fluorescence is a promising technique for postharvest quality research, and reported studies were mainly based on the fluorescence kinetic analysis method, which has not been quite satisfactory for measuring fruit quality. This paper reports on using a hyperspectral imaging technique for measurement of laser-induced fluorescence from apple fruit for predicting multiple fruit quality parameters. A continuous wave blue laser (408 nm) was used as an excitation source for inducing fluorescence in apples. Fluorescence scattering images were acquired from ‘Golden Delicious’ apples by a hyperspectral imaging system at the instance of laser illumination (0 min) and after 1, 2, 3, 4, and 5 min of illumination. The hyperspectral fluorescence image data were represented by mean, maximum, and standard deviation spectra. Standard tests were performed on measuring fruit skin and flesh color, firmness, soluble solids content, and titratable acid. A hybrid method of combining principal component analysis and neural network modeling was used for developing prediction models to predict fruit quality parameters for each of the six illumination time periods. Fluorescence emission decreased steadily during the first 3 min of illumination and was stable within 5 min. The 0-min fluorescence prediction models had somewhat poorer prediction results for individual quality parameters except skin hue than did the models of longer illumination time. The differences were minimal in the model prediction results from the fluorescence data at 1, 2, 3, 4 or 5 min of illumination. Overall, excellent predictions were obtained for apple skin hue with the correlation coefficient of prediction of 0.94. Relatively good predictions were obtained for fruit firmness, skin chroma, and flesh hue with values for the correlation coefficient being equal to or greater than 0.74 for 1 min of illumination or longer times, and poorer correlations were found for soluble solids content, titratable acid, and flesh chroma. Hyperspectral laser-induced fluorescence imaging is potentially useful for assessing selected quality parameters of apple fruit and further research is needed to improve fluorescence measurement for better prediction of fruit quality. |
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