Journal
MEAT SCIENCE
Volume 123, Issue -, Pages 182-191Publisher
ELSEVIER SCI LTD
DOI: 10.1016/j.meatsci.2016.09.017
Keywords
Muscle food; HSI; Technological parameter; Chemometrics; Classification
Categories
Funding
- International S&T Cooperation Program of China [2015DFA71150]
- Natural Science Foundation of Guangdong Province [2014A030313244]
- Key Projects of Administration of Ocean and Fisheries of Guangdong Province [A201401C04]
- Collaborative Innovation Major Special Projects of Guangzhou City [201508020097]
- International S&T Cooperation Projects of Guangdong Province [2013B051000010]
- International and Hong Kong - Macau - Taiwan Collaborative Innovation Platform of Guangdong Province on Intelligent Food Quality Control and Process Technology Equipment [2015KGJHZ001]
- Guangdong Provincial R & D Centre for the Modern Agricultural Industry on Non-destructive Detection and Intensive Processing of Agricultural Products
- Common Technical Innovation Team of Guangdong Province on Preservation and Logistics of Agricultural Products [2016LM2154]
- Guangdong Province Government (China) through the program Leading Talent of Guangdong Province (Da-Wen Sun)
- China Scholarship Council (CSC) at KU Leuven in Belgium
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Muscle foods are very important for a well-balanced daily diet Due to their perishability and vulnerability, there is a need for quality and safety evaluation of such foods. Hyperspectral imaging (HSI) coupled with multivariate analysis is becoming increasingly popular for the non-destructive, non-invasive, and rapid determination of important quality attributes and the classification of muscle foods. This paper reviews recent advances of application of HSI for predicting some significant muscle foods parameters, including color, tenderness, firmness, springiness, water-holding capacity, drip loss and pH. In addition, algorithms for the rapid classification of muscle foods are also reported and discussed. It will be shown that this technology has great potential to replace traditional analytical methods for predicting various quality parameters and classifying muscle foods. (C) 2016 Elsevier Ltd. All rights reserved.
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