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Food Analytical Methods

, Volume 12, Issue 9, pp 1988–1997 | Cite as

A Clustering-Based Partial Least Squares Method for Improving the Freshness Prediction Model of Crucian Carps Fillets by Hyperspectral Image Technology

  • Xue Wang
  • Mohammad Russel
  • Yiwen Zhang
  • Junbo Zhao
  • Yituo Zhang
  • Jiajia ShanEmail author
Article

Abstract

Hyperspectral imaging technology (HSI) is able to visualize the distribution map of chemicals in samples in combination with a developed prediction model. Generally, prediction models are established based on the spectrum and the chemical reference averagely calculated/measured from the sole area covering the sample. However, uneven chemical distribution is widely observed in the individual sample. The uneven distribution of chemicals may result in the unspecific match between the spectrum and chemical reference, which were averagely achieved from the non-homogeneous sample, leading to low robustness model. The aim of this work was to improve the performance of the freshness prediction models of fillets by eliminating the effect of uneven chemical distribution in each fillet. This study proposed a clustering-based partial least squares (C-PLS) algorithm, which firstly divided a non-homogeneous fillet into several relatively homogeneous sub-pieces using cluster analysis. Spectra and freshness indices were averagely acquired from the sub-pieces respectively, aiming to find a more specific match between the spectra and chemical indices. Compared with the partial least squares regression model, C-PLS model performed a higher coefficient of determination of cross-validation for the prediction of total volatile basic nitrogen (TVB-N), pH, and water holding capacity (WHC) of the fillet, which would be a benefit for precisely monitoring fish quality online.

Keywords

Cluster analysis Hyperspectral image Fillets Freshness Models improvement 

Notes

Acknowledgments

The authors thanks National Natural Science Foundation of China [grant number: 31701691]; the Fundamental Research Funds for the Central Universities [DUT17RC (4)41]; and Open Foundation of State Key Laboratory of Environmental Criteria and Risk Assessment, Chinese Research Academy of Environmental Sciences [grant number: SKLECRA2017OFP02] for the financial support.

Compliance with Ethical Standards

Conflict of Interest

Xue Wang declares that there is no conflict of interest. Mohammad Russel declares that there is no conflict of interest. Yiwen Zhang declares that there is no conflict of interest. Junbo Zhao declares that there is no conflict of interest. Jiajia Shan declares that there is no conflict of interest.

Ethical Approval

All applicable international, national, or institutional guidelines for the care and use of animals were followed.

Informed Consent

Not applicable.

Supplementary material

12161_2019_1541_MOESM1_ESM.docx (28 kb)
ESM 1 (DOCX 27 kb)

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Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2019

Authors and Affiliations

  1. 1.School of Food and EnvironmentDalian University of TechnologyPanjinChina

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