期刊
FOOD ENGINEERING REVIEWS
卷 9, 期 1, 页码 36-49出版社
SPRINGER
DOI: 10.1007/s12393-016-9147-1
关键词
PLSR; NIR; Hyperspectral imaging; Chemometrics; Chemical information; Fish
资金
- Natural Science Foundation of Guangdong Province [2014A030313244]
- International S&T Cooperation Program of China [2015DFA71150BC]
- 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 Program of Guangdong Province [2013B051000010]
- Guangdong Province Government (China) through the program Leading Talent of Guangdong Province (Da-Wen Sun)
- China Scholarship Council (CSC)
Partial least squares regression (PLSR) is a classical and widely used linear method for modeling of spectral data. Measurement of fish chemical properties has been playing an important role in providing superior quality products for human health and international trade. This review focuses on the PLSR applied to near-infrared (NIR) and hyperspectral imaging (HSI) spectral data for rapid and chemical-free modeling and predicting chemical properties of fish muscle, including moisture content, lipid content, protein content, pH, and freshness indicators, such as total volatile basic nitrogen, thiobarbituric acid reactive substances, and K index value. Furthermore, the commonly used spectral preprocessing methods and variable selection algorithms are mentioned and discussed for the enhancement of PLSR analysis. The limitations and future trends of NIR and HSI techniques with PLSR analysis are also presented. In a word, NIR and HSI technique in tandem with PLSR method have been developed to be suitable and trustworthy alternatives to the traditional chemical analytical methods such as Kjeldahl, Soxhlet, and chromatography methods for detecting chemical information of fish muscle in an objective, rapid, noninvasive, and chemical-free manner.
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