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Partial Least Squares Regression (PLSR) Applied to NIR and HSI Spectral Data Modeling to Predict Chemical Properties of Fish Muscle

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Abstract

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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Acknowledgments

This research was supported by the financial support of the Natural Science Foundation of Guangdong Province (2014A030313244), the International S&T Cooperation Program of China (2015DFA71150BC), the Key Projects of Administration of Ocean and Fisheries of Guangdong Province (A201401C04), the Collaborative Innovation Major Special Projects of Guangzhou City (201508020097), and the International S&T Cooperation Program of Guangdong Province (2013B051000010). The authors also gratefully acknowledge the financial support of Guangdong Province Government (China) through the program “Leading Talent of Guangdong Province (Da-Wen Sun)” and the financial support provided by China Scholarship Council (CSC) for supporting Jun-Hu Cheng’s PhD study at KU Leuven in Belgium.

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Authors and Affiliations

  1. School of Food Science and Engineering, South China University of Technology, Guangzhou, 510641, China

    Jun-Hu Cheng & Da-Wen Sun

  2. Academy of Contemporary Food Engineering, Guangzhou Higher Education Mega Center, South China University of Technology, Guangzhou, 510006, China

    Jun-Hu Cheng & Da-Wen Sun

  3. MeBioS, Department of Biosystems, KU Leuven, Willem de Croylaan 42, 3001, Heverlee, Belgium

    Jun-Hu Cheng

  4. Food Refrigeration and Computerized Food Technology (FRCFT), Agriculture and Food Science Centre, University College Dublin, National University of Ireland, Belfield, Dublin 4, Ireland

    Da-Wen Sun

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  1. Jun-Hu Cheng
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Correspondence to Da-Wen Sun.

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Cheng, JH., Sun, DW. Partial Least Squares Regression (PLSR) Applied to NIR and HSI Spectral Data Modeling to Predict Chemical Properties of Fish Muscle. Food Eng Rev 9, 36–49 (2017). https://doi.org/10.1007/s12393-016-9147-1

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