Differentiation of South African Game Meat Using Near-Infrared (NIR) Spectroscopy and Hierarchical Modelling.
chemometrics
game meat
hierarchical modelling
meat fraud
near-infrared spectroscopy
partial least squares discriminant analysis (PLS-DA)
spectral analysis
Journal
Molecules (Basel, Switzerland)
ISSN: 1420-3049
Titre abrégé: Molecules
Pays: Switzerland
ID NLM: 100964009
Informations de publication
Date de publication:
16 Apr 2020
16 Apr 2020
Historique:
received:
17
03
2020
revised:
10
04
2020
accepted:
13
04
2020
entrez:
23
4
2020
pubmed:
23
4
2020
medline:
26
1
2021
Statut:
epublish
Résumé
Near-infrared (NIR) spectroscopy, combined with multivariate data analysis techniques, was used to rapidly differentiate between South African game species, irrespective of the treatment (fresh or previously frozen) or the muscle type. These individual classes (fresh; previously frozen; muscle type) were also determined per species, using hierarchical modelling. Spectra were collected with a portable handheld spectrophotometer in the 908-1676-nm range. With partial least squares discriminant analysis models, we could differentiate between the species with accuracies ranging from 89.8%-93.2%. It was also possible to distinguish between fresh and previously frozen meat (90%-100% accuracy). In addition, it was possible to distinguish between ostrich muscles (100%), as well as the forequarters and hindquarters of the zebra (90.3%) and springbok (97.9%) muscles. The results confirm NIR spectroscopy's potential as a rapid and non-destructive method for species identification, fresh and previously frozen meat differentiation, and muscle type determination.
Identifiants
pubmed: 32316308
pii: molecules25081845
doi: 10.3390/molecules25081845
pmc: PMC7221759
pii:
doi:
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Subventions
Organisme : South African Research Chairs Initiative (SARChI)
ID : 94031
Organisme : South African Department of Science and Technology
ID : 106172
Organisme : National Research Foundation (NRF)
ID : 84633
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