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

Qiao, Tong and Ren, Jinchang and Craigie, Cameron and Zabalza, Jaime and Maltin, Charlotte and Marshall, Stephen (2015) Singular spectrum analysis for improving hyperspectral imaging based beef eating quality evaluation. Computers and Electronics in Agriculture, 115. pp. 21-25. ISSN 0168-1699

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    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.

    Creators(s): Qiao, Tong ORCID logoORCID: https://orcid.org/0000-0001-7527-7897, Ren, Jinchang ORCID logoORCID: https://orcid.org/0000-0001-6116-3194, Craigie, Cameron, Zabalza, Jaime ORCID logoORCID: https://orcid.org/0000-0002-0634-1725, Maltin, Charlotte and Marshall, Stephen ORCID logoORCID: https://orcid.org/0000-0001-7079-5628;
    Item type:Article
    ID code:53175
    Keywords:hyperspectral imaging, beef quality prediction, singular spectrum analysis, principal component analysis, support vector machine, Electrical engineering. Electronics Nuclear engineering, Electrical and Electronic Engineering
    Subjects:Technology > Electrical engineering. Electronics Nuclear engineering
    Department:Faculty of Engineering > Electronic and Electrical Engineering
    Technology and Innovation Centre > Sensors and Asset Management
    Depositing user: Pure Administrator
    Date deposited:02 Jun 2015 08:32
    Last modified:20 Jan 2021 22:08
    Related URLs:
    URI:https://strathprints.strath.ac.uk/id/eprint/53175
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