Abstract
Lamb eating quality is related to 3 factors, which are tenderness, juiciness and flavour. In addition to these factors, the surface colour of lamb could influence the purchase decision of consumers. Objective quality evaluation approaches, like near infrared spectroscopy (NIRS) and hyperspectral imaging (HSI), have been proved fast and non-destructive in assessing beef quality, compared with conventional methods. However, rare research has been done for lamb samples. Therefore, in this paper the feasibility of HSI for evaluating lamb quality is tested. A total of 80 lamb samples were imaged using a visible range HSI system and the spectral profiles were used for predicting lamb quality related traits. For some traits, noises were removed from HSI spectra by singular spectrum analysis (SSA) for better performance. Support vector machine (SVM) was employed to construct prediction equations. Considering SVM is sensitive to high dimensional data, principal component analysis (PCA) was applied to reduce the dimensionality first. The prediction results suggest that HSI is promising in predicting lamb eating quality
Original language | English |
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Title of host publication | OCM (Optical Characterization of Materials) 2015 |
Subtitle of host publication | 2nd International Conference on Optical Characterization of Materials |
Editors | Jürgen Beyerer, Fernando Puente León, Thomas Längle |
Place of Publication | Karlsruhe, Germany |
Pages | 15-25 |
Number of pages | 10 |
DOIs | |
Publication status | Published - 15 Mar 2015 |
Event | OCM (Optical Characterization of Materials) 2015 - Karlsruhe, Germany Duration: 18 Mar 2015 → 19 Mar 2015 |
Conference
Conference | OCM (Optical Characterization of Materials) 2015 |
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Abbreviated title | OCM 2015 |
Country/Territory | Germany |
City | Karlsruhe |
Period | 18/03/15 → 19/03/15 |
Keywords
- hyperspectral imaging (HSI)
- singular spectrum analysis (SSA)
- principal component analysis (PCA)
- lamb eating quality
- tenderness
- juiciness
- flavour