Research Output per year
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.
- hyperspectral imaging
- beef quality prediction
- singular spectrum analysis
- principal component analysis
- support vector machine
Novel two dimensional singular spectrum analysis for effective feature extraction and data classification in hyperspectral imagingZabalza, 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 journal › Article