Picture water droplets

Developing mathematical theories of the physical world: Open Access research on fluid dynamics from Strathclyde

Strathprints makes available Open Access scholarly outputs by Strathclyde's Department of Mathematics & Statistics, where continuum mechanics and industrial mathematics is a specialism. Such research seeks to understand fluid dynamics, among many other related areas such as liquid crystals and droplet evaporation.

The Department of Mathematics & Statistics also demonstrates expertise in population modelling & epidemiology, stochastic analysis, applied analysis and scientific computing. Access world leading mathematical and statistical Open Access research!

Explore all Strathclyde Open Access research...

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

[img]
Preview
Text (Qiao-etal-CEA-2015-Singular-spectrum-analysis-for-improving-hyperspectral-imaging-based-beef-eating)
Qiao_etal_CEA_2015_Singular_spectrum_analysis_for_improving_hyperspectral_imaging_based_beef_eating.pdf
Accepted Author Manuscript
License: Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 logo

Download (916kB) | Preview

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.