| Literature DB >> 28231437 |
Stuart O J Crichton1, Sascha M Kirchner1, Victoria Porley2, Stefanie Retz1, Gardis von Gersdorff1, Oliver Hensel1, Martin Weygandt3, Barbara Sturm4.
Abstract
Consumer trust in the food industry is heavily reliant upon accurate labelling of meat products. As such, methods, which can verify whether meat is correctly labelled are of great value to producers, retailers, and consumers. This paper illustrates two approaches to classify between, fresh and frozen thawed, and in a novel manner matured and matured frozen-thawed, as well as fresh and matured beef using the 500-1010nm waveband, captured using hyperspectral imaging, and CIELAB measurements. The results show successful classification based upon CIELAB between 1) fresh and frozen-thawed (CCR=0.93), and 2) fresh and matured (CCR=0.92). With successful classification between matured and matured frozen-thawed beef using the entire spectral range (CCR=1.00). The performance of reduced spectral models is also investigated. Overall it was found that CIELAB co-ordinates can be used for successful classification for all comparisons except between matured and matured frozen-thawed. Biochemical and physical changes of the meat are thoroughly discussed for each condition.Entities:
Keywords: Beef; Chromaticity; Classification; Freezing; Hyperspectral; Maturation; Quality; SVM; Storage; Support vector machines; VNIR
Mesh:
Year: 2017 PMID: 28231437 DOI: 10.1016/j.meatsci.2017.02.005
Source DB: PubMed Journal: Meat Sci ISSN: 0309-1740 Impact factor: 5.209