Journal
JOURNAL OF FOOD ENGINEERING
Volume 182, Issue -, Pages 9-17Publisher
ELSEVIER SCI LTD
DOI: 10.1016/j.jfoodeng.2016.02.004
Keywords
Multispectral imaging; Grass carp; Chemical spoilage; LS-SVM; GA; Freshness
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Funding
- Natural Science Foundation of Guangdong Province [2014A030313244]
- Key Projects of Administration of Ocean and Fisheries of Guangdong Province [A201401C04]
- International S&T Cooperation Program of China [2015DFA71150]
- National Key Technologies RD Program [2014BAD08B09]
- International S&T Cooperation Program of Guangdong Province [2013B051000010]
- Guangdong Province Government (China) through the program Leading Talent of Guangdong Province
- China Scholarship Council (CSC) at KU Leuven in Belgium
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This study investigated the feasibility of developing a multispectral imaging method using key wavelengths from hyperspectral images for modeling and simultaneously predicting total volatile basic nitrogen (TVB-N), thiobarbituric acid reactive substances (TBARS) and K value in grass carp fillet during chemical spoilage. The established least-squares support vector machine (LS-SVM) and multiple linear regression (MLR) models using five successive projection algorithm (SPA)-selected and six genetic algorithm (GA)-selected wavelengths showed excellent performances for predicting TVB-N and K value with R(2)p > 0.900 and RPD > 3.000, and poor results for TBARS value prediction. The LS-SVM model using six GA-selected wavelengths showed good reliability and was considered the best for simultaneous determination of TVB-N, TBARS and K value. The distribution maps of chemical spoilage changes were generated using image processing algorithms. The results demonstrated the feasibility of developing a rapid and on-line multispectral imaging system using the feature wavelengths and chemometrics analysis. (C) 2016 Elsevier Ltd. All rights reserved.
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