Food Analytical Methods

, Volume 10, Issue 6, pp 1888–1898 | Cite as

Enhancing Visible and Near-Infrared Hyperspectral Imaging Prediction of TVB-N Level for Fish Fillet Freshness Evaluation by Filtering Optimal Variables



Total volatile basic nitrogen (TVB-N) is one of the most important indicators for evaluation of fish protein degradation and freshness loss. A novel algorithm of Physarum network (PN) combined with genetic algorithm (GA) was developed to select optimal wavelengths from hyperspectral images for enhancing the TVB-N level prediction in grass carp fish fillet. Partial least squares regression (PLSR) and least squares support vector machines (LS-SVM) calibration models were built using six optimal wavelengths selected by the PN-GA method and the PN-GA-PLSR model showed better performance for predicting the TVB-N value with determination coefficient in prediction (R 2 P ) of 0.956 and root mean square errors in prediction (RMSEP) of 1.737 mg N/100 g. The PN-GA-PLSR model established using the optimal wavelengths and image texture variables extracted by gray-level gradient co-occurrence matrix (GLGCM) algorithm showed higher R 2 P of 0.981 and lower RMSEP of 1.435 mg N/100 g. The results indicated that the PN-GA method was a good technique for selecting optimal wavelengths for enhancing prediction ability of hyperspectral imaging, which also demonstrated the efficiency and usefulness of this method for monitoring the freshness degree during fish cold storage.


Hyperspectral imaging TVB-N value Physarum network Genetic algorithm Wavelength selection Grass carp 



The authors are grateful to the International S&T Cooperation Program of China (2015DFA71150) for its support. This research was also supported by the Collaborative Innovation Major Special Projects of Guangzhou City (201508020097, 201604020007, 201604020057), the Guangdong Provincial Science and Technology Plan Projects (2015A020209016, 2016A040403040), the Key Projects of Administration of Ocean and Fisheries of Guangdong Province (A201401C04), the National Key Technologies R&D Program (2015BAD19B03), the International and Hong Kong - Macau - Taiwan Collaborative Innovation Platform of Guangdong Province on Intelligent Food Quality Control and Process Technology & Equipment (2015KGJHZ001), the Guangdong Provincial R & D Centre for the Modern Agricultural Industry on Non-destructive Detection and Intensive Processing of Agricultural Products and the Common Technical Innovation Team of Guangdong Province on Preservation and Logistics of Agricultural Products (2016LM2154). The authors also gratefully acknowledged the Guangdong Province Government (China) for its support through the program “Leading Talent of Guangdong Province (Da-Wen Sun)”.

Compliance with Ethical Standards

Conflict of Interest

Jun-Hu Cheng declares that he has no conflict of interest. Da-Wen Sun declares that he has no conflict of interest. Qingyi Wei declares that she has no conflict of interest.

Ethical Approval

This article does not contain any studies with human participants performed by any of the authors.

Informed Consent

Not applicable.


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Copyright information

© Springer Science+Business Media New York 2016

Authors and Affiliations

  1. 1.School of Food Science and EngineeringSouth China University of TechnologyGuangzhouChina
  2. 2.Academy of Contemporary Food Engineering (ACFE), South China University of TechnologyGuangzhou Higher Education Mega CenterGuangzhouChina
  3. 3.Engineering and Technological Research Centre of Guangdong Province on Intelligent Sensing and Process Control of Cold Chain FoodsGuangzhou Higher Education Mega CentreGuangzhouChina
  4. 4.Food Refrigeration and Computerized Food Technology (FRCFT), Agriculture and Food Science CentreUniversity College Dublin, National University of IrelandBelfieldIreland

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