Abstract
| Original language | English |
|---|---|
| Pages (from-to) | 21-25 |
| Number of pages | 5 |
| Journal | Computers and Electronics in Agriculture |
| Volume | 115 |
| Early online date | 26 May 2015 |
| DOIs | |
| Publication status | Published - Jul 2015 |
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Keywords
- hyperspectral imaging
- beef quality prediction
- singular spectrum analysis
- principal component analysis
- support vector machine
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Singular spectrum analysis for improving hyperspectral imaging based beef eating quality evaluation. / Qiao, Tong; Ren, Jinchang; Craigie, Cameron; Zabalza, Jaime; Maltin, Charlotte; Marshall, Stephen.
In: Computers and Electronics in Agriculture, Vol. 115, 07.2015, p. 21-25.Research output: Contribution to journal › Article
TY - JOUR
T1 - Singular spectrum analysis for improving hyperspectral imaging based beef eating quality evaluation
AU - Qiao, Tong
AU - Ren, Jinchang
AU - Craigie, Cameron
AU - Zabalza, Jaime
AU - Maltin, Charlotte
AU - Marshall, Stephen
PY - 2015/7
Y1 - 2015/7
N2 - 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.
AB - 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.
KW - hyperspectral imaging
KW - beef quality prediction
KW - singular spectrum analysis
KW - principal component analysis
KW - support vector machine
UR - http://www.sciencedirect.com/science/article/pii/S0168169915001295#
U2 - 10.1016/j.compag.2015.05.007
DO - 10.1016/j.compag.2015.05.007
M3 - Article
VL - 115
SP - 21
EP - 25
JO - Computers and Electronics in Agriculture
JF - Computers and Electronics in Agriculture
SN - 0168-1699
ER -