Detection of Adulteration in Red Meat Species Using Hyperspectral Imaging

  • Mahmoud Al-SarayrehEmail author
  • Marlon M. Reis
  • Wei Qi Yan
  • Reinhard KletteEmail author
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10749)


This paper reports the performance of hyperspectral imaging for detecting the adulteration in red-meat species. Line-scanning images are acquired from muscles of lamb, beef, or pork. We consider the states of fresh, frozen, or thawed meat. For each case, packing and unpacking the sample with a transparent bag is considered and evaluated. Meat muscles are defined either as a class of lamb, or as a class of beef or pork. For visualization purposes, fat regions are also considered. We investigate raw spectral features, normalized spectral features, and a combination of spectral and spatial features by using texture properties. Results show that adding texture features to normalized spectral features achieves the best performance, with a 92.8% overall classification accuracy independently of the state of the products. The resulting model provides a high and balanced sensitivity for all classes at all meat stages. The resulting model yields 94% and 90% average sensitivities for detecting lamb or the other meat type, respectively. This paper shows that hyperspectral imaging analysis provides a rapid, reliable, and non-destructive method for detecting the adulteration in red-meat products.


Hyperspectral imaging Spectral-spatial features Meat classification Meat processing Adulteration detection 



Authors appreciate funding by the AgResearch Core Fund and the Ministry of Business, Innovation and Employment, New Zealand. Authors also acknowledge conference support by Auckland University of Technology, the School of Engineering, Computer and Mathematical Sciences.


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Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  1. 1.School of Engineering, Computer and Mathematical SciencesAuckland University of TechnologyAucklandNew Zealand
  2. 2.AgResearchPalmerston NorthNew Zealand

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