Journal
FOOD CHEMISTRY
Volume 192, Issue -, Pages 134-141Publisher
ELSEVIER SCI LTD
DOI: 10.1016/j.foodchem.2015.06.106
Keywords
Hyperspectral reflectance imaging; Peach; Cold injury; Optimal wavelength; Artificial neural network; Detection
Funding
- Chinese National Foundation of Nature and Science (NSFC) [31101282, 71103086]
- Special Fund for Agro-scientific Research in the Public Interest of China [201303088]
- National Key Technology R&D Program of China [2015BAD19B03]
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Peaches in cold storage may develop chill damage, as symptomized by deteriorated texture and lack of juice. To examine fruit quality, we established a hyperspectral imaging system to detect cold injury, and an artificial neural network (ANN) model was developed for which eight optimal wavelengths were selected. Between normal and chill-damaged peaches, significant differences in fruit quality parameters and the spectral response to correlating selected wavelengths were observed. Evidencing this relationship, the correlation coefficients between quality parameters and the respective spectral response of eight selected wavelengths were 0.587 to 0.700, 0.393 to 0.552, 0.510 to 0.751, and 0.574 to 0.773. With optimal representative wavelengths as inputs for the ANN model, the overall classification accuracy of chill damage was 95.8% for all cold-stored samples. The ANN prediction models for quality parameters performed well, with correlation coefficients from 0.6979 to 0.9026. This research demonstrates feasibility of hyperspectral reflectance imaging technique for detecting cold injury. (C) 2015 Elsevier Ltd. All rights reserved.
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