Detail
Article
Online article
FT
Medvik - BMC
  • Something wrong with this record ?

Identification of individual goat animals by means of near infrared spectroscopy and chemometrics analysis of commercial meat cuts

D. Cozzolino, S. Zhang, A. Khole, Z. Yang, P. Ingle, M. Beya, PF. van Jaarsveld, D. Bureš, LC. Hoffman

. 2024 ; 61 (5) : 950-957. [pub] 20231110

Status not-indexed Language English Country India

Document type Journal Article

E-resources Online Full text

NLK Free Medical Journals from 2010 to 1 year ago
PubMed Central from 2010 to 1 year ago
Europe PubMed Central from 2010 to 1 year ago
ProQuest Central from 2010-01-01 to 1 year ago

Although the identification of animal species and muscles have been reported previously, no studies have been found on the use of NIR spectroscopy to identify individual animals from the analysis of commercial meat cuts. The aim of this study was to evaluate the use of a portable near infrared (NIR) instrument combined with classical chemometrics methods [principal component analysis (PCA) and partial least squares discriminant analysis PLS-DA)] to identify the origin of individual goat animals using the spectral signature of their commercial cut. Samples were collected from several carcasses (6 commercial cuts x 24 animals) sourced from a commercial abattoir in Queensland (Australia). The NIR spectra of the samples were collected using a portable NIR instrument in the wavelength range between 950 and 1600 nm. Overall, the PLS-DA models correctly classify 82% and 79% of the individual goat samples using either the goat rack or loin cut samples, respectively. The study demonstrated that NIR spectroscopy was able to identify individual goat animals based on the spectra properties of some of the commercial cut samples analysed (e.g. loin and rack). These results showed the potential of this technique to identify individual animals as an alternative to other laboratory methods and techniques commonly used in meat traceability.

References provided by Crossref.org

000      
00000naa a2200000 a 4500
001      
bmc24005504
003      
CZ-PrNML
005      
20240412130954.0
007      
ta
008      
240405s2024 ii f 000 0|eng||
009      
AR
024    7_
$a 10.1007/s13197-023-05890-1 $2 doi
035    __
$a (PubMed)38487278
040    __
$a ABA008 $b cze $d ABA008 $e AACR2
041    0_
$a eng
044    __
$a ii
100    1_
$a Cozzolino, D $u Centre for Nutrition and Food Sciences (CNAFS), The University of Queensland, Queensland Alliance for Agriculture and Food Innovation (QAAFI), Brisbane, QLD 4072 Australia $u The University of Queensland, School of Agriculture and Food Sciences, Brisbane, QLD 4072 Australia
245    10
$a Identification of individual goat animals by means of near infrared spectroscopy and chemometrics analysis of commercial meat cuts / $c D. Cozzolino, S. Zhang, A. Khole, Z. Yang, P. Ingle, M. Beya, PF. van Jaarsveld, D. Bureš, LC. Hoffman
520    9_
$a Although the identification of animal species and muscles have been reported previously, no studies have been found on the use of NIR spectroscopy to identify individual animals from the analysis of commercial meat cuts. The aim of this study was to evaluate the use of a portable near infrared (NIR) instrument combined with classical chemometrics methods [principal component analysis (PCA) and partial least squares discriminant analysis PLS-DA)] to identify the origin of individual goat animals using the spectral signature of their commercial cut. Samples were collected from several carcasses (6 commercial cuts x 24 animals) sourced from a commercial abattoir in Queensland (Australia). The NIR spectra of the samples were collected using a portable NIR instrument in the wavelength range between 950 and 1600 nm. Overall, the PLS-DA models correctly classify 82% and 79% of the individual goat samples using either the goat rack or loin cut samples, respectively. The study demonstrated that NIR spectroscopy was able to identify individual goat animals based on the spectra properties of some of the commercial cut samples analysed (e.g. loin and rack). These results showed the potential of this technique to identify individual animals as an alternative to other laboratory methods and techniques commonly used in meat traceability.
590    __
$a NEINDEXOVÁNO
655    _2
$a časopisecké články $7 D016428
700    1_
$a Zhang, S $u Centre for Nutrition and Food Sciences (CNAFS), The University of Queensland, Queensland Alliance for Agriculture and Food Innovation (QAAFI), Brisbane, QLD 4072 Australia $u The University of Queensland, School of Agriculture and Food Sciences, Brisbane, QLD 4072 Australia
700    1_
$a Khole, A $u Centre for Nutrition and Food Sciences (CNAFS), The University of Queensland, Queensland Alliance for Agriculture and Food Innovation (QAAFI), Brisbane, QLD 4072 Australia $u The University of Queensland, School of Agriculture and Food Sciences, Brisbane, QLD 4072 Australia
700    1_
$a Yang, Z $u Centre for Nutrition and Food Sciences (CNAFS), The University of Queensland, Queensland Alliance for Agriculture and Food Innovation (QAAFI), Brisbane, QLD 4072 Australia $u The University of Queensland, School of Agriculture and Food Sciences, Brisbane, QLD 4072 Australia
700    1_
$a Ingle, P $u Centre for Nutrition and Food Sciences (CNAFS), The University of Queensland, Queensland Alliance for Agriculture and Food Innovation (QAAFI), Brisbane, QLD 4072 Australia $u The University of Queensland, School of Agriculture and Food Sciences, Brisbane, QLD 4072 Australia
700    1_
$a Beya, M $u Centre for Nutrition and Food Sciences (CNAFS), The University of Queensland, Queensland Alliance for Agriculture and Food Innovation (QAAFI), Brisbane, QLD 4072 Australia $u The University of Queensland, School of Agriculture and Food Sciences, Brisbane, QLD 4072 Australia
700    1_
$a van Jaarsveld, P F $u Centre for Nutrition and Food Sciences (CNAFS), The University of Queensland, Queensland Alliance for Agriculture and Food Innovation (QAAFI), Brisbane, QLD 4072 Australia $u The University of Queensland, School of Agriculture and Food Sciences, Brisbane, QLD 4072 Australia
700    1_
$a Bureš, D $u Institute of Animal Science, 104 00 Přátelství 815, 104 00 Prague, Czech Republic $u Department of Food Science, Faculty of Agrobiology, Food and Natural Resources, Czech University of Life Sciences Prague, 165 00 Prague, Czech Republic
700    1_
$a Hoffman, L C $u Centre for Nutrition and Food Sciences (CNAFS), The University of Queensland, Queensland Alliance for Agriculture and Food Innovation (QAAFI), Brisbane, QLD 4072 Australia $u The University of Queensland, School of Agriculture and Food Sciences, Brisbane, QLD 4072 Australia
773    0_
$w MED00008417 $t Journal of food science and technology $x 0022-1155 $g Roč. 61, č. 5 (2024), s. 950-957
856    41
$u https://pubmed.ncbi.nlm.nih.gov/38487278 $y Pubmed
910    __
$a ABA008 $b sig $c sign $y - $z 0
990    __
$a 20240405 $b ABA008
991    __
$a 20240412130947 $b ABA008
999    __
$a ok $b bmc $g 2075938 $s 1215266
BAS    __
$a 3
BAS    __
$a PreBMC-PubMed-not-MEDLINE
BMC    __
$a 2024 $b 61 $c 5 $d 950-957 $e 20231110 $i 0022-1155 $m Journal of food science and technology $n J Food Sci Technol $x MED00008417
LZP    __
$a Pubmed-20240405

Find record

Citation metrics

Loading data ...

Archiving options

Loading data ...