Detection of Adulteration in Red Meat Species Using Hyperspectral Imaging

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aut.relation.conferencePacific-Rim Symposium Image Video Technology (PSIVT)en_NZ
aut.relation.endpage13
aut.relation.pages13
aut.relation.startpage1
aut.researcherYan, Wei-Qi
dc.contributor.authorAl-Sarayreh, Men_NZ
dc.contributor.authorReis, Men_NZ
dc.contributor.authorYan, W-Qen_NZ
dc.contributor.authorKlette, Ren_NZ
dc.date.accessioned2019-09-16T02:51:51Z
dc.date.available2019-09-16T02:51:51Z
dc.date.copyright2017-11-20en_NZ
dc.date.issued2017-11-20en_NZ
dc.description.abstractThis 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.
dc.identifier.citationImage and Video Technology. PSIVT 2017. Lecture Notes in Computer Science, vol 10749. Springer, Cham
dc.identifier.doi10.1007/978-3-319-75786-5_16
dc.identifier.urihttps://hdl.handle.net/10292/12817
dc.publisherSpringeren_NZ
dc.relation.urihttps://link.springer.com/chapter/10.1007/978-3-319-75786-5_16#Abs1
dc.rightsAn author may self-archive an author-created version of his/her article on his/her own website and or in his/her institutional repository. He/she may also deposit this version on his/her funder’s or funder’s designated repository at the funder’s request or as a result of a legal obligation, provided it is not made publicly available until 12 months after official publication. He/ she may not use the publisher's PDF version, which is posted on www.springerlink.com, for the purpose of self-archiving or deposit. Furthermore, the author may only post his/her version provided acknowledgement is given to the original source of publication and a link is inserted to the published article on Springer's website. The link must be accompanied by the following text: "The final publication is available at www.springerlink.com”. (Please also see Publisher’s Version and Citation).
dc.rights.accessrightsOpenAccessen_NZ
dc.subjectHyperspectral imaging; Spectral-spatial features; Meat classification; Meat processing; Adulteration detection
dc.titleDetection of Adulteration in Red Meat Species Using Hyperspectral Imagingen_NZ
dc.typeConference Contribution
pubs.elements-id309673
pubs.organisational-data/AUT
pubs.organisational-data/AUT/Design & Creative Technologies
pubs.organisational-data/AUT/Design & Creative Technologies/Engineering, Computer & Mathematical Sciences
pubs.organisational-data/AUT/PBRF
pubs.organisational-data/AUT/PBRF/PBRF Design and Creative Technologies
pubs.organisational-data/AUT/PBRF/PBRF Design and Creative Technologies/PBRF ECMS
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