Journal
FOOD CHEMISTRY
Volume 197, Issue -, Pages 855-863Publisher
ELSEVIER SCI LTD
DOI: 10.1016/j.foodchem.2015.11.019
Keywords
Multispectral imaging; Grass carp; Frozen-thawed; Variable selection; LS-SVM
Funding
- Guangdong Province Government (China)
- Natural Science Foundation of Guangdong Province [2014A030313244]
- National Key Technologies RD Program [2014BAD08B09]
- International S&T Cooperation Program of China [2015DFA71150]
- International S&T Cooperation Program of Guangdong Province [2013B051000010]
- Key Projects of Administration of Ocean and Fisheries of Guangdong Province [A201401C04]
- China Scholarship Council (CSC)
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The potential use of feature wavelengths for predicting drip loss in grass carp fish, as affected by being frozen at -20 degrees C for 24 h and thawed at 4 degrees C for 1, 2, 4, and 6 days, was investigated. Hyperspectral images of frozen-thawed fish were obtained and their corresponding spectra were extracted. Least-squares support vector machine and multiple linear regression (MLR) models were established using five key wavelengths, selected by combining a genetic algorithm and successive projections algorithm, and this showed satisfactory performance in drip loss prediction. The MLR model with a determination coefficient of prediction (R-P(2)) of 0.9258, and lower root mean square error estimated by a prediction (RMSEP) of 1.12%, was applied to transfer each pixel of the image and generate the distribution maps of exudation changes. The results confirmed that it is feasible to identify the feature wavelengths using variable selection methods and chemometric analysis for developing on-line multispectral imaging. (C) 2015 Elsevier Ltd. All rights reserved.
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