Food and Bioprocess Technology

, Volume 6, Issue 10, pp 2931–2937 | Cite as

Application of Visible and Near Infrared Hyperspectral Imaging to Differentiate Between Fresh and Frozen–Thawed Fish Fillets

  • Fengle Zhu
  • Derong Zhang
  • Yong He
  • Fei Liu
  • Da-Wen Sun
Communication
First Online:
Received:
Accepted:

Abstract

The potential of visible and near infrared (VIS/NIR) hyperspectral imaging was investigated as a rapid and nondestructive technique to determine whether fish has been frozen–thawed. A total of 108 halibut (Psetta maxima) fillets were studied, including 48 fresh and 60 frozen–thawed (F-T) samples. Regarding the F-T samples, two speeds of freezing (fast and slow) were tested. The hyperspectral images of fillets were captured using a pushbroom hyperspectral imaging system in the spectral region of 380 to 1,030 nm. All images were calibrated for reflectance, followed by the minimum noise fraction rotation to reduce the noise. A region-of-interest (ROI) at the image center was selected, and the average spectral data were generated from the ROI image. Dimension reduction was carried out on the ROI image by principal component analysis. The first three principal components (PCs) explained over 98 % of variances of all spectral bands. Gray-level co-occurrence matrix analysis was implemented on the three PC images to extract 36 textural feature variables in total. Least squares-support vector machine classification models were developed to differentiate between fresh and F-T fish based on (1) spectral variables; (2) textural variables; (3) combined spectral and textural variables, respectively. Satisfactory average correct classification rate of 97.22 % for the prediction samples based on (3) was achieved, which was superior to the results based on (1) or (2). The results turned worse when different freezing rates were taken into consideration to classify three groups of fish. The overall results indicate that VIS/NIR hyperspectral imaging technique is promising for the reliable differentiation between fresh and F-T fish.

Keywords

Fresh fish Frozen–thawed fish Differentiation Hyperspectral imaging Least squares-support vector machine 
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Notes

Acknowledgments

This study was supported by the 863 National High-Tech Research and Development Plan (Project no: 2011AA100705) and the Fundamental Research Funds for the Central Universities.

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Copyright information

© Springer Science+Business Media, LLC 2012

Authors and Affiliations

  • Fengle Zhu
    • 1
  • Derong Zhang
    • 2
  • Yong He
    • 1
  • Fei Liu
    • 1
  • Da-Wen Sun
    • 3
  1. 1.College of Biosystems Engineering and Food ScienceZhejiang UniversityHangzhouPeople’s Republic of China
  2. 2.Ningbo Institute of TechnologyZhejiang UniversityNingboChina
  3. 3.Food Refrigeration and Computerised Food TechnologyNational University of IrelandBelfieldIreland

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