Singular spectrum analysis for improving hyperspectral imaging based beef eating quality evaluation

Tong Qiao, Jinchang Ren, Cameron Craigie, Jaime Zabalza, Charlotte Maltin, Stephen Marshall

Research output: Contribution to journalArticle

30 Citations (Scopus)
84 Downloads (Pure)

Abstract

Detecting beef eating quality in a non-destructive way has been popular in recent years. Among various non-destructive assessing methods, the feasibility of hyperspectral imaging (HSI) system was investigated in this paper. Hyperspectral images of beef samples were collected in an abattoir production line and used for predicting the beef tenderness and pH value. Support vector machine (SVM) was applied to construct the prediction equation. Before utilizing the original HSI spectral profiles directly, we propose to use singular spectrum analysis (SSA) as a pre-processing approach, where SSA has been proven to be an effective technique for time-series analysis in diverse applications. The results indicate that SSA can remove the instrumental noise of HSI system effectively and therefore improve the prediction performance.
Original languageEnglish
Pages (from-to)21-25
Number of pages5
JournalComputers and Electronics in Agriculture
Volume115
Early online date26 May 2015
DOIs
Publication statusPublished - Jul 2015

Keywords

  • hyperspectral imaging
  • beef quality prediction
  • singular spectrum analysis
  • principal component analysis
  • support vector machine

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  • Research Output

    • 30 Citations
    • 1 Article

    Novel two dimensional singular spectrum analysis for effective feature extraction and data classification in hyperspectral imaging

    Zabalza, J., Ren, J., Zheng, J., Han, J., Zhao, H., Li, S. & Marshall, S., 31 Aug 2015, In : IEEE Transactions on Geoscience and Remote Sensing. 53, 8, p. 4418-4433 16 p.

    Research output: Contribution to journalArticle

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  • 75 Citations (Scopus)
    305 Downloads (Pure)

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