Online Prediction of Physico-Chemical Quality Attributes of Beef Using Visible-Near-Infrared Spectroscopy and Chemometrics.
meat
monitoring
near-infrared spectroscopy
on-line
quality
Journal
Foods (Basel, Switzerland)
ISSN: 2304-8158
Titre abrégé: Foods
Pays: Switzerland
ID NLM: 101670569
Informations de publication
Date de publication:
23 Oct 2019
23 Oct 2019
Historique:
received:
06
09
2019
revised:
15
10
2019
accepted:
16
10
2019
entrez:
27
10
2019
pubmed:
28
10
2019
medline:
28
10
2019
Statut:
epublish
Résumé
The potential of visible-near-infrared (Vis-NIR) spectroscopy to predict physico-chemical quality traits in 368 samples of bovine musculus longissimus thoracis et lumborum (LTL) was evaluated. A fibre-optic probe was applied on the exposed surface of the bovine carcass for the collection of spectra, including the neck and rump (1 h and 2 h post-mortem and after quartering, i.e., 24 h and 25 h post-mortem) and the boned-out LTL muscle (48 h and 49 h post-mortem). In parallel, reference analysis for physico-chemical parameters of beef quality including ultimate pH, colour (L, a*, b*), cook loss and drip loss was conducted using standard laboratory methods. Partial least-squares (PLS) regression models were used to correlate the spectral information with reference quality parameters of beef muscle. Different mathematical pre-treatments and their combinations were applied to improve the model accuracy, which was evaluated on the basis of the coefficient of determination of calibration (R
Identifiants
pubmed: 31652829
pii: foods8110525
doi: 10.3390/foods8110525
pmc: PMC6915407
pii:
doi:
Types de publication
Journal Article
Langues
eng
Subventions
Organisme : Department of Agriculture, Food and the Marine
ID : 11/SF/311
Références
Meat Sci. 2004 Jun;67(2):219-29
pubmed: 22061317
Meat Sci. 2018 Apr;138:53-58
pubmed: 29331862
Meat Sci. 2016 Mar;113:124-31
pubmed: 26656871
Meat Sci. 2016 Feb;112:52-7
pubmed: 26519609
Meat Sci. 2015 Feb;100:69-72
pubmed: 25306513
Food Chem. 2016 Aug 15;205:14-22
pubmed: 27006208
Poult Sci. 2011 Jul;90(7):1594-9
pubmed: 21673177
Meat Sci. 2013 Aug;94(4):455-60
pubmed: 23618741
Meat Sci. 2020 Jan;159:107915
pubmed: 31470197
Meat Sci. 2018 May;139:15-24
pubmed: 29367118
Meat Sci. 2010 Sep;86(1):214-26
pubmed: 20579814
Food Chem. 2018 Nov 30;267:223-230
pubmed: 29934161
J Food Sci Technol. 2017 Aug;54(9):2852-2860
pubmed: 28928525
Meat Sci. 2008 Aug;79(4):692-9
pubmed: 22063031
Proteomics. 2013 May;13(9):1528-44
pubmed: 23456991
J Anim Sci. 2001 Mar;79(3):678-87
pubmed: 11263828
Foods. 2019 May 21;8(5):null
pubmed: 31117235
Meat Sci. 2008 Mar;78(3):217-24
pubmed: 22062273
Meat Sci. 2009 Sep;83(1):96-103
pubmed: 20416617
Meat Sci. 2018 Mar;137:58-66
pubmed: 29154219
Meat Sci. 2009 Dec;83(4):672-7
pubmed: 20416640
PeerJ. 2018 Aug 6;6:e5376
pubmed: 30123708
Meat Sci. 2018 Feb;136:59-67
pubmed: 29096288
Crit Rev Food Sci Nutr. 2017 Mar 4;57(4):755-768
pubmed: 25975703
Analyst. 2016 Mar 7;141(5):1587-610
pubmed: 26835653
Meat Sci. 2008 Nov;80(3):697-702
pubmed: 22063585
Meat Sci. 2001 Aug;58(4):395-401
pubmed: 22062430