Reference : Mid-infrared prediction of bovine milk fatty acids across multiple breeds, production...
Scientific journals : Article
Life sciences : Animal production & animal husbandry
Life sciences : Food science
Mid-infrared prediction of bovine milk fatty acids across multiple breeds, production systems, and countries
[fr] Prédiction infrarouge des contenus en acides gras du lait par une approche multi-races, pays et systèmes de production
Soyeurt, Hélène mailto [Université de Liège - ULg > Sciences agronomiques > Zootechnie >]
Dehareng, mailto [ > > ]
Gengler, Nicolas mailto [Université de Liège - ULg > Sciences agronomiques > Zootechnie >]
McParland, Sinead [Teagasc - Moorepark (Ireland) > > > >]
Wall, Eileen [Scottish Agricultural College > > > >]
Berry, Donagh [Teagasc - Moorepark (Ireland) > > > >]
Coffey, Mike [Scottish Agricultural College > > > >]
Dardenne, Pierre mailto [ > > ]
Journal of Dairy Science
American Dairy Science Association
Yes (verified by ORBi)
[en] mid-infrared ; milk ; fatty acid
[fr] infrarouge ; lait ; acide gras
[en] Increasing consumer concern exists over the relationship
between food composition and human health.
Because of the known effects of fatty acids on human
health, the development of a quick, inexpensive, and
accurate method to directly quantify the fatty acid
(FA) composition in milk would be valuable for milk
processors to develop a payment system for milk pertinent
to their customer requirements and for farmers to
adapt their feeding systems and breeding strategies accordingly.
The aim of this study was (1) to confirm the
ability of mid-infrared spectrometry (MIR) to quantify
individual FA content in milk by using an innovative
procedure of sampling (i.e., samples were collected from
cows belonging to different breeds, different countries,
and in different production systems); (2) to compare
6 mathematical methods to develop robust calibration
equations for predicting the contents of individual FA
in milk; and (3) to test interest in using the FA equations
developed in milk as basis to predict FA content
in fat without corrections for the slope and the bias
of the developed equations. In total, 517 samples selected
based on their spectral variability in 3 countries
(Belgium, Ireland, and United Kingdom) from various
breeds, cows, and production systems were analyzed
by gas chromatography (GC). The samples presenting
the largest spectral variability were used to calibrate
the prediction of FA by MIR. The remaining samples
were used to externally validate the 28 FA equations
developed. The 6 methods were (1) partial least squares
regression (PLS); (2) PLS + repeatability file (REP);
(3) first derivative of spectral data + PLS; (4) first
derivative + REP + PLS; (5) second derivative of spectral
data + PLS; and (6) second derivative + REP +
PLS. Methods were compared on the basis of the crossvalidation
coefficient of determination (R2cv), the ratio
of standard deviation of GC values to the standard
error of cross-validation (RPD), and the validation coefficient
of determination (R2v). The third and fourth
methods had, on average, the highest R2cv, RPD, and
R2v. The final equations were built using all GC and
the best accuracy was observed for the infrared predictions
of C4:0, C6:0, C8:0, C10:0, C12:0, C14:0, C16:0,
C18:0, C18:1 trans, C18:1 cis-9, C18:1 cis, and for some
groups of FA studied in milk (saturated, monounsaturated,
unsaturated, short-chain, medium-chain, and
long-chain FA). These equations showed R2cv greater
than 0.95. With R2cv equal to 0.85, the MIR prediction
of polyunsaturated FA could be used to screen the cow
population. As previously published, infrared predictions
of FA in fat are less accurate than those developed
from FA content in milk (g/dL of milk) and no better
results were obtained by using milk FA predictions if no
corrections for bias and slope based on reference milk
samples with known contents of FA were used. These
results indicate the usefulness of equations with R2cv
greater than 95% in milk payment systems and the
usefulness of equations with R2cv greater than 75% for
animal breeding purposes.
Researchers ; Professionals ; Students

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