Visible hyperspectral imaging for lamb quality prediction

Tong Qiao, Jinchang Ren*, Zhijing Yang, Chunmei Qing, Jaime Zabalza, Stephen Marshall

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

3 Citations (Scopus)
22 Downloads (Pure)


Three factors, including tenderness, juiciness and flavour, are found to have an impact on lamb eating quality, which determines the repurchase behaviour of customers. In addition to these factors, the surface colour of lamb can also influence the purchase decision of consumers. From a long time ago, meat industries have been looking for fast and non-invasive objective quality evaluation approaches, where near-infrared spectroscopy (NIRS) and hyperspectral imaging (HSI) have shown great promises in assessing beef quality compared with conventional methods. However, rare research has been conducted for lamb samples. Therefore, in this paper the feasibility of the HSI system for evaluating lamb quality was tested. In total 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, noise was further removed from HSI spectra by singular spectrum analysis (SSA) for better performance. Considering support vector machine (SVM) is sensitive to high dimensional data, principal component analysis (PCA) was applied to reduce the dimensionality of HSI spectra before feeding into SVM for constructing prediction equations. The prediction results suggest that HSI is promising in predicting some lamb eating quality traits, which could be beneficial for lamb industries.

Original languageEnglish
Pages (from-to)643-652
Number of pages10
JournalTechnisches Messen
Issue number12
Early online date1 Dec 2015
Publication statusPublished - 28 Dec 2015


  • hyperspectral imaging
  • lamb quality
  • principal component analysis
  • singular spectral analysis
  • support vector machine.


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