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  •   Open Research
  • AUT Faculties
  • Faculty of Design and Creative Technologies (Te Ara Auaha)
  • School of Engineering, Computer and Mathematical Sciences - Te Kura Mātai Pūhanga, Rorohiko, Pāngarau
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Detection of Adulteration in Red Meat Species Using Hyperspectral Imaging

Al-Sarayreh, M; Reis, M; Yan, W-Q; Klette, R
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http://hdl.handle.net/10292/12817
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Abstract
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.
Keywords
Hyperspectral imaging; Spectral-spatial features; Meat classification; Meat processing; Adulteration detection
Date
November 20, 2017
Source
Image and Video Technology. PSIVT 2017. Lecture Notes in Computer Science, vol 10749. Springer, Cham
Item Type
Conference Contribution
Publisher
Springer
DOI
10.1007/978-3-319-75786-5_16
Publisher's Version
https://link.springer.com/chapter/10.1007/978-3-319-75786-5_16#Abs1
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