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Validation of fatty acid predictions in milk using mid-infrared spectrometry across cattle breeds

July 2012
animal 7(2):1-7

July 2012
7(2):1-7

DOI:10.1017/S1751731112001218

Source
PubMed

License
CC BY-NC-ND 4.0

Authors:

M.H.T. Maurice-van Eijndhoven

Wageningen University & Research

hélène Soyeurt

University of Liège

Frédéric Dehareng

Walloon Agricultural Research Centre CRA-W

Mario P L Calus

Wageningen University & Research

The aim of this study was to investigate the accuracy to predict detailed fatty acid (FA) composition of bovine milk by mid-infrared spectrometry, for a cattle population that partly differed in terms of country, breed and methodology used to measure actual FA composition compared with the calibration data set. Calibration equations for predicting FA composition using mid-infrared spectrometry were developed in the European project RobustMilk and based on 1236 milk samples from multiple cattle breeds from Ireland, Scotland and the Walloon Region of Belgium. The validation data set contained 190 milk samples from cows in the Netherlands across four breeds: Dutch Friesian, Meuse-Rhine-Yssel, Groningen White Headed (GWH) and Jersey (JER). The FA measurements were performed using gas–liquid partition chromatography (GC) as the gold standard. Some FAs and groups of FAs were not considered because of differences in definition, as the capillary column of the GC was not the same as used to develop the calibration equations. Differences in performance of the calibration equations between breeds were mainly found by evaluating the standard error of validation and the average prediction error. In general, for the GWH breed the smallest differences were found between predicted and reference GC values and least variation in prediction errors, whereas for JER the largest differences were found between predicted and reference GC values and most variation in prediction errors. For the individual FAs 4:0, 6:0, 8:0, 10:0, 12:0, 14:0 and 16:0 and the groups’ saturated FAs, short-chain FAs and medium-chain FAs, predictions assessed for all breeds together were highly accurate (validation R 2 > 0.80) with limited bias. For the individual FAs cis-14:1, cis-16:1 and 18:0, the calibration equations were moderately accurate (R 2 in the range of 0.60 to 0.80) and for the individual FA 17:0 predictions were less accurate (R 2 < 0.60) with considerable bias. FA concentrations in the validation data set of our study were generally higher than those in the calibration data. This difference in the range of FA concentrations, mainly due to breed differences in our study, can cause lower accuracy. In conclusion, the RobustMilk calibration equations can be used to predict most FAs in milk from the four breeds in the Netherlands with only a minor loss of accuracy.

The predicted content of the individual fatty acid 16:0 based on mid-infrared spectometry plotted against the reference gas chromatography values.

…

Descriptive statistics of RobustMilk FA calibration equations and the data used to derive the equations

…

The predicted content of the individual fatty acid cis-16:1 based

…

The RPD v 1 of 11 FAs and 3 groups of FAs for different dairy breeds

…

mean and standard deviation of gas chromatographic measurements of the validation data for all traits of the individual breeds

…

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Animal

(2013), 7:2, pp 348–354 &The Animal Consortium 2012

doi:10.1017/S1751731112001218

animal

Validation of fatty acid predictions in milk using mid-infrared

spectrometry across cattle breeds

M. H. T. Maurice-Van Eijndhoven

1,2

, H. Soyeurt

3,4

, F. Dehareng

and M. P. L. Calus

Animal Breeding and Genomics Centre, Wageningen UR Livestock Research, PO Box 65, 8200 AB Lelystad, The Netherlands;

Animal Breeding and Genomics

Centre, Wageningen University, PO Box 338, 6700 AH Wageningen, The Netherlands;

Animal Science Unit, Gembloux Agro-Bio Tech, University of Lie

`ge, 5030

Gembloux, Belgium;

National Fund for Scientiﬁc Research, 1000 Brussels, Belgium;

Valorisation of Agricultural Products, Walloon Agricultural Research Centre,

5030 Gembloux, Belgium

(Received 1 March 2012; Accepted 11 May 2012; First published online 2 July 2012)

The aim of this study was to investigate the accuracy to predict detailed fatty acid (FA) composition of bovine milk by mid-infrared

spectrometry, for a cattle population that partly differed in terms of country, breed and methodology used to measure actual FA

composition compared with the calibration data set. Calibration equations for predicting FA composition using mid-infrared

spectrometry were developed in the European project RobustMilk and based on 1236 milk samples from multiple cattle breeds

from Ireland, Scotland and the Walloon Region of Belgium. The validation data set contained 190 milk samples from cows in the

Netherlands across four breeds: Dutch Friesian, Meuse-Rhine-Yssel, Groningen White Headed (GWH) and Jersey (JER). The FA

measurements were performed using gas–liquid partition chromatography (GC) as the gold standard. Some FAs and groups of

FAs were not considered because of differences in deﬁnition, as the capillary column of the GC was not the same as used to

develop the calibration equations. Differences in performance of the calibration equations between breeds were mainly found by

evaluating the standard error of validation and the average prediction error. In general, for the GWH breed the smallest differences

were found between predicted and reference GC values and least variation in prediction errors, whereas for JER the largest

differences were found between predicted and reference GC values and most variation in prediction errors. For the individual

FAs 4:0, 6:0, 8:0, 10:0, 12:0, 14:0 and 16:0 and the groups’ saturated FAs, short-chain FAs and medium-chain FAs, predictions

assessed for all breeds together were highly accurate (validation

.0.80) with limited bias. For the individual FAs

cis

-14:1,

cis

-16:1 and 18:0, the calibration equations were moderately accurate (

in the range of 0.60 to 0.80) and for the individual FA

17:0 predictions were less accurate (

,0.60) with considerable bias. FA concentrations in the validation data set of our study

were generally higher than those in the calibration data. This difference in the range of FA concentrations, mainly due to breed

differences in our study, can cause lower accuracy. In conclusion, the RobustMilk calibration equations can be used to predict

most FAs in milk from the four breeds in the Netherlands with only a minor loss of accuracy.

Keywords: milk, fatty acid, mid-infrared spectrometry, cattle breeds

Implications

Measurement of detailed milk fat composition at individual

cow level is of major interest for the dairy industry because

of the expected relation with human health. Therefore, the

method of analyzing milk fat composition needs to be rapid

and suitable for extensive recording. Our study shows that

mid-infrared spectrometry (MIR) can be used to accurately

predict detailed milk fat composition from different cattle

breeds in the Netherlands.

Introduction

Bovine milk fat consists of a range of different fatty acids

(FAs), both unsaturated fatty acids (UFAs) and saturated

fatty acids (SFAs), and its relatively large amount of SFA

causes some debate about the role of bovine milk in

a healthy diet (Palmquist

et al.

, 2006). Clear variation in

fat content and milk fat composition can be found among

cows (Soyeurt and Gengler, 2008). Milk fat composition

varies with both environmental factors (e.g. feed regime;

Palmquist, 2006) and genetics (Soyeurt and Gengler, 2008;

Stoop

et al.

, 2008). Changing FA composition through the

feed regime or genetic selection requires a precise and

regular measurement.

Present address: Wageningen UR Livestock Research, Vijfde Polder 1, 6708 WC

Wageningen, The Netherlands. E-mail: myrthe.maurice-vaneijndhoven@wur.nl

348

To measure the FA composition in milk, several methods can

be used, which differ in throughput level, accuracy, workload

and costs. The most accurate method is gas–liquid partition

chromatography (GC). This largely implemented and regularly

used approach quantiﬁes the concentration of individual FAs in

fat (Gander

et al.

, 1962; Christie, 1998). The major advantage of

GC is the possibility of measuring the individual FA proportions

with high accuracy (Smith, 1961; Christie, 1998) even if the

content of this FA is low. This method, however, is expensive and

time consuming and therefore less suitable for extensive and

regular recording. Another method of analyzing milk fat com-

position, MIR, is rapid and less expensive in case of extensive

use (Wilson and Tapp, 1999; Soyeurt

et al.

, 2006). MIR is routi-

nely used in milk recording schemes to measure lactose, urea,

total fat and protein percentages in bovine milk (Etzion

et al.

2004; Bobe

et al.

, 2007). MIR was used by Soyeurt

et al.

(2006

and 2011) and Rutten

et al.

(2009) to estimate calibration

equations predicting the FA concentrations in milk (g/dl of milk)

and milk fat (g/100 g of fat), and these equations were subse-

quently validated. In these studies, the predictions have low

accuracy for FAs that are present in low concentrations, such as

the

trans

and unsaturated 14, 16 and 18 FAs.

Accuracy and bias in calibration equations may also be

affected when there are differences between the samples used

to estimate the calibration equations and the samples for which

FA composition is predicted using the prediction equations.

In Rutten

et al.

(2009), calibration equations were based on

milk samples only from Holstein–Friesian (HF) cows. In this

latter study, analysis of milk samples collected in both winter

and summer indicated that season has a limited effect on

prediction accuracy but generally a large effect on prediction

bias. This indicates that factors causing structural differences

between FA composition of groups of animals such as season

and breed can affect predictability of calibration equations.

The aim of this study was to investigate the accuracy and

bias in predicting detailed FA composition from MIR spectra

of milk from four cattle breeds in the Netherlands, using

calibration equations based on milk samples collected from

Belgian, Irish and Scottish cattle of partly different breeds.

Material and Methods

Calibration equations

In this study, prediction of the composition of 11 individual

FAs and 3 groups of FAs using MIR spectrometry using cali-

bration equations was validated. These calibration equations

were developed in the EU FP 7 project RobustMilk, using a

data set with MIR spectra and GC results of 1236 milk

samples. The methodology used to develop the calibration

equations is explained by Soyeurt

et al.

(2011) for the cali-

bration equations, but it should be noted that in our study

updated versions of the calibrations were used, which are

based on 1236 instead of 517 milk samples.

For all 1236 milk samples, the MIR analysis was performed

using a Fourier-transformed interferogram with a region of

1000 to 5000/cm (MilkoScan FT 6000, Foss Electric, Hillerod,

Denmark). The detailed FA composition of these 1236 milk

samples was obtained using GC realized at the milk laboratory of

the Walloon Agricultural Research Centre (Gembloux, Belgium).

The GC outputs were generated by analyzing methyl esters pre-

pared from milk fat as described in ISO Standard 15 884 (ISO–IDF

(International Organization for Standardization–Interna-

tional Dairy Federation), 2002) and the GC was equipped

with a CPSil-88 column (Varian Inc., Palo Alto, CA, USA) with

a length of 100 m and an internal diameter of 0.25 mm.

The 1236 milk samples were collected from herds in Ire-

land, Scotland and the Walloon Region of Belgium with

purebred and crossbred cows from different breeds, that is,

HF, Jersey (JER), Red and White, Normande, Montbeliarde

and dual-purpose Belgian Blue. This multiple breed and

multiple country composition of the data set was chosen to

cover a wide range of the variability of FA in bovine milk in

order to improve the robustness of the developed calibration

equations. The calibration equations were developed from

three MIR regions located between 926 and 1600/cm, 1712

and 1809/cm and 2561 and 2989/cm. The method used to

relate MIR spectra to FA data was partial least square

regression after a ﬁrst derivative pre-treatment on spectral

data to correct the baseline drift. A

-outlier test was also

used during the calibration process to delete potential GC

outliers. Therefore, the ﬁnal number of samples included in

each calibration equation varied following the considered

FA. Descriptive statistics of the RobustMilk calibration

equations are given in Table 1. Note that this is an updated

version of the prediction equations described by Soyeurt

et al.

(2011), in the sense that the current prediction equa-

tions are based on ,4.5 times more samples. In addition to

the number of samples included in the calibration data set,

the mean and the standard deviation (s.d.) of the FA content

measured by GC, the standard error of calibration (SEC), the

calibration coefﬁcient of determination (

c), standard error

of cross-validation (SECV), cross-validation coefﬁcient of

determination (

cv) and the ratio of s.d. to SECV (RPD) are

shown. The

c is the square of the correlation coefﬁcient

between the predicted and the reference GC values.

During the development of the updated calibration

equations, a ﬁrst assessment of the robustness of the pre-

dictions was done by a cross-validation approach to calculate

the

cv and SECV using the same approach as described by

Soyeurt

et al.

(2011).

Validation data set

Between December 2008 and March 2009 in the Nether-

lands, that is, in the winter season, a total of 190 cows were

sampled once during morning milking. Samples were treated

immediately with 0.03% (w/w) sodium azide to avoid

microbiological growth. Cows belonged to four breeds:

Dutch Friesian (DF; 47 samples from 3 farms), Meuse-Rhine-

Yssel (MRY; 52 samples from 3 farms), Groningen White

Headed (GWH; 45 samples from 3 farms) and JER (46 sam-

ples from 3 farms). The cows were selected by farmers to

reﬂect variations in age, parity, stage of lactation and ancestry.

On all farms, cows were kept indoors in the studied period

and milked twice a day with conventional milking systems.

Validation of milk fatty acid prediction

349

The number of sampled cows per herd ranged from 6 to 24, and

the selected farms each had between 35 and 120 cows. The

cows were either located at organic or conventional farms. For

each breed samples were collected at one or two organic farms

and the remainder farms were conventional. Differences in FA

composition in milk between the four breeds in this data set

are presented by Maurice-Van Eijndhoven

et al.

(2011).

Brieﬂy, ranges of individual FA content generally overlapped

between breeds, apart from several FAs and groups of FAs of

JER and GWH.

Each milk sample was analyzed using both GC and MIR.

The mean and standard deviation of the FA content of each

of the 11 individual FAs and the 3 groups of FAs obtained

using the GC are given in Table 2. The relative variability,

which wasexamined by calculating the coefﬁcient of variation

(results not shown), between the different FAs was highest

for the

cis

-14:1 (range 31.1 to 35.8) and

cis

-16:1 (range 26.2

to 39.1) and lowest for the 4:0, 6:0, 8:0 and total group

of short-chain FA (SCFA; range 14.6 to 25.0). GC analysis

was performed at the laboratory of Qlip N.V. (Leusden, The

Netherlands). The GC outputs were generated by analyzing

methyl esters prepared from milk fat as described in ISO

Standard 15 884 (ISO–IDF, 2002) and the GC was equipped

with a Varian Fame Select CP 7420 column (Varian Inc., Palo

Alto, CA, USA) with a length of 100 m and an internal dia-

meter of 0.25 mm. The MIR analysis was performed using a

Fourier-transformed interferogram with a region of 1000 to

5000/cm (MilkoScan FT 6000, Foss Electric, Denmark) at the

laboratory of Qlip N.V. (Zutphen, The Netherlands). The

validation data set was independent of the calibration set

Table 1

Descriptive statistics of RobustMilk FA calibration equations and the data used to derive the equations

Trait (g/dl of milk)

Mean s.d. SEC

c SECV

cv RPD

4:0 1186 0.101 0.030 0.008 0.93 0.008 0.93 3.68

6:0 1189 0.074 0.023 0.005 0.96 0.005 0.96 4.81

8:0 1180 0.048 0.015 0.003 0.96 0.003 0.96 5.00

10:0 1183 0.112 0.036 0.007 0.96 0.008 0.96 4.72

12:0 1180 0.134 0.044 0.009 0.96 0.010 0.95 4.61

14:0 1184 0.448 0.130 0.027 0.96 0.028 0.95 4.70

cis

-14:1 1180 0.040 0.015 0.007 0.80 0.007 0.78 2.13

16:0 1179 1.206 0.424 0.066 0.98 0.068 0.97 6.20

cis

-16:1 1179 0.067 0.023 0.010 0.79 0.011 0.78 2.14

17:0 1167 0.028 0.008 0.002 0.90 0.003 0.89 3.04

18:0 1173 0.375 0.145 0.043 0.91 0.045 0.90 3.24

SFA 1176 2.689 0.785 0.050 1.00 0.051 1.00 15.34

SCFA 1185 0.349 0.104 0.020 0.96 0.020 0.96 5.10

MCFA 1187 2.056 0.645 0.082 0.98 0.086 0.98 7.53

FA 5fatty acid;

5number of samples included in the calibration equation; Mean 5mean of gas chromatographic data; s.d. 5standard deviation of gas

chromatographic data; SEC 5standard error of calibration;

c5calibration coefﬁcient of determination; SECV 5standard error of cross-validation;

cv 5cross-

validation coefﬁcient of determination; RPD 5the ratio of s.d. to SECV; SFA 5the saturated FAs 4:0 to 22:0 including iso- and ante-iso FAs; SCFA 5short-chain FAs

4:0 to 10:0; MCFA 5medium-chain FAs 12:0 to 16:0.

Table 2

The mean and standard deviation of gas chromatographic measurements of the validation data for all traits of the

individual breeds

Trait (g/dl of milk) GWH (mean 6s.d.) MRY (mean 6s.d.) DF (mean 6s.d.) JER (mean 6s.d.)

4:0 0.131 60.020 0.124 60.024 0.130 60.023 0.171 60.028

6:0 0.093 60.014 0.098 60.019 0.104 60.017 0.133 60.024

8:0 0.059 60.011 0.072 60.015 0.072 60.011 0.088 60.018

10:0 0.138 60.031 0.174 60.046 0.186 60.033 0.224 60.057

12:0 0.184 60.051 0.244 60.066 0.230 60.047 0.273 60.076

14:0 0.563 60.094 0.637 60.140 0.617 60.114 0.782 60.151

cis

-14:1 0.052 60.019 0.055 60.019 0.044 60.014 0.061 60.019

16:0 1.483 60.275 1.472 60.305 2.260 60.442 1.522 60.353

cis

-16:1 0.065 60.017 0.057 60.019 0.058 60.018 0.098 60.028

17:0 0.027 60.008 0.024 60.006 0.023 60.005 0.037 60.007

18:0 0.495 60.150 0.517 60.109 0.524 60.100 0.697 60.118

SFA 3.332 60.503 3.522 60.671 3.563 60.632 4.876 60.817

SCFA 0.436 60.069 0.482 60.101 0.507 60.074 0.637 60.119

MCFA 2.500 60.439 2.621 60.546 2.619 60.537 3.674 60.709

GWH 5Groningen White Headed; MRY 5Meuse-Rhine-Yssel; DF 5Dutch Friesian; JER 5Jersey; FA 5fatty acid; SFA 5the saturated FAs

4:0 to 22:0 including iso- and ante-iso FAs; SCFA 5short-chain FAs 4:0 to 10:0; MCFA 5medium-chain FAs 12:0 to 16:0.

Maurice-Van Eijndhoven, Soyeurt, Dehareng and Calus

350

developed in the RobustMilk project (i.e. different labs for

GC and MIR analysis).

Validation

RobustMilk calibration equations (Table 1) were used to

predict detailed milk composition of the samples recorded in

the validation data set for 11 individual FAs 4:0, 6:0, 8:0,

10:0, 12:0, 14:0,

cis

-14:1, 16:0,

cis

-16:1, 17:0, 18:0 and the 3

groups of FAs, that is, total SFA (SFA 4:0 to 22:0 including

iso- and ante-iso FAs), short-chain FA (SCFA; 4:0 to 10:0) and

medium-chain FA (MCFA; 12:0 to 16:0). SFA 5the saturated

fatty acids 4:0 to 22:0 including iso- and ante-iso FAs;

SCFA 54:0 to 10:0; MCFA 512:0 to 16:0. Owing to the lack

of agreement between the GC analyses of the calibration

and validation data set methods for the long-chain unsatu-

rated FAs and their related FA groups (i.e. total unsaturated,

monounsaturated, polyunsaturated and long-chain FAs),

these FAs and groups of FAs were considered in this study. This

lack of agreement was due to differences in separation of the

long-chain unsaturated FAs during the GC analyses of the

calibration and validation data sets because the capillary col-

umns used were different. For the other FAs, of which the

calibration equations are validated in this study, the separation

during the GC analysis was similar. The FA traits were predicted

on the basis of milk (g/dl) because these predictions are more

accurate than on the basis of fat (g/100 g; Soyeurt

et al.

, 2006;

Rutten

et al.

, 2009; Soyeurt

et al.

, 2011).

The accuracy of the RobustMilk predictions was evaluated

using the root mean squared error of prediction (SEV), the

coefﬁcient of determination (validation

) and the ratio of

the s.d. of the validation data set to the SEV (RPD

). Cali-

bration equations with RPD

above 3.0 can be considered as

good predictors (Williams and Sobering, 1993).

The SEV was calculated as

SEV ¼ﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃ

i¼1

ð^

yiyiÞ2

where ^

yiis the predicted value obtained for the sample

;

is the reference GC value of sample

;

is the number of

samples in the validation set. The approach to calculate SEV

is in line with the approach to calculate SEC and SECV, which

are described by Soyeurt

et al.

(2011).

The prediction bias was assessed using the average pre-

diction error ð^

yiyiÞand slope (

) of the linear regression,

with the GC values as dependent and the predicted values as

independent variable. To be able to compare the average

prediction error of the calibration equations across traits and

breeds, this measure is expressed as a percentage of the

mean of the gas chromatography values.

Results

For most traits, the SEV was lowest for the predicted FA

contents in GWH milk, except for the individual FA

cis

-14:0

and the group of FA SCFA (Table 3). The SEV for the predicted

FA contents in JER milk was highest for all groups of FAs and

the individual FAs 6:0, 14:0,

cis

-14:0 and 16:0, except for the

individual FAs 4:0, 12:0,

cis

-16:1, 17:0 and 18:0, of which the

SEV was highest for MRY. The validation

of the predictions

of the individual FAs 4:0, 6:0, 8:0, 10:0, 12:0, 14:0, 16:0 and

for the groups of FAs SFA, SCFA and MCFA for all breeds

were above 0.80 (Table 4). The validation

for the individual

Table 3

The SEV of 11 FAs and 3 groups of FAs for different dairy breeds

Breed

Trait (g/dl of milk) GWH MRY DF JER All breeds

4:0 0.009 0.017 0.011 0.011 0.012

6:0 0.005 0.006 0.006 0.007 0.006

8:0 0.004 0.005 0.006 0.006 0.005

10:0 0.012 0.016 0.025 0.022 0.019

12:0 0.027 0.048 0.036 0.028 0.036

14:0 0.033 0.042 0.037 0.045 0.039

cis

-14:1 0.011 0.010 0.014 0.022 0.015

16:0 0.122 0.201 0.215 0.219 0.192

cis

-16:1 0.023 0.040 0.033 0.028 0.032

17:0 0.010 0.013 0.012 0.010 0.012

18:0 0.106 0.145 0.139 0.137 0.132

SFA 0.061 0.047 0.050 0.130 0.078

SCFA 0.022 0.028 0.028 0.033 0.028

MCFA 0.105 0.171 0.207 0.253 0.190

SEV 5standard error of validation; FA 5fatty acid; GWH 5Groningen White

Headed; MRY 5Meuse-Rhine-Yssel; DF 5Dutch Friesian; JER 5Jersey; SFA 5the

saturated FAs 4:0 to 22:0 including iso- and ante-iso FAs; SCFA 5short-chain FAs

4:0 to 10:0; MCFA5medium-chain FAs 12:0 to 16:0.

Breeds total is the SEV of the predictions across the breeds GWH, DF, MRY

and JER.

Table 4

The validation

of prediction of 11 FAs and 3 groups of FAs

for different dairy breeds

Breed

Trait (g/dl of milk) GWH MRY DF JER All breeds

4:0 0.92 0.92 0.89 0.88 0.92

6:0 0.90 0.92 0.88 0.91 0.93

8:0 0.88 0.90 0.88 0.91 0.92

10:0 0.85 0.94 0.89 0.93 0.93

12:0 0.85 0.86 0.80 0.90 0.85

14:0 0.93 0.97 0.93 0.92 0.95

cis

-14:1 0.70 0.76 0.79 0.80 0.64

16:0 0.89 0.90 0.93 0.86 0.93

cis

-16:1 0.56 0.67 0.48 0.59 0.65

17:0 0.15 0.17 0.73 0.24 0.43

18:0 0.80 0.64 0.65 0.58 0.72

SFA 0.99 1.00 1.00 0.98 0.99

SCFA 0.91 0.93 0.93 0.93 0.95

MCFA 0.95 0.97 0.97 0.92 0.96

FA 5fatty acid; GWH 5Groningen White Headed; MRY 5Meuse-Rhine-Yssel;

DF 5Dutch Friesian; JER 5Jersey; SFA 5the saturated FAs 4:0 to 22:0

including iso- and ante-iso FAs; SCFA 5short-chain FAs 4:0 to 10:0;

MCFA 5medium-chain FAs 12:0 to 16:0.

Breeds total is the

of the predictions across the breeds GWH, DF, MRY

and JER.

Validation of milk fatty acid prediction

351

FA 17:0 was lowest over all breeds (0.43). The FA composition

of milk from DF cows was based on the calculated validation

predicted most accurately with an average

of 0.84. The

average validation

of the predicted FA composition of milk

from GWH was generally lowest (0.81). The long-chain FAs 17:0

and 18:0 and medium-chain FAs

cis

-14:1 and

cis

-16:1 showed

the largest variation in validation

between the breeds. The

RPD

was in general lower than the RPD of the cross-validation;

however, a similar trend was observed (Table 5). The RPD

above 3.0 across all breeds (breeds total) for 6:0; 8:0; 14:0 and

all groups of FAs.

Bias was examined by calculating the average predic-

tion error and the slope of the linear regression with the GC

values as dependent and the predicted values as indepen-

dent variable (Tables 6 and 7). To be able to compare the

average prediction error of the calibration equations

between traits and breeds, the values were expressed as a

percentage of the mean of the gas chromatography absolute

value (Table 6). The bias in terms of average prediction error

for individual breeds is largest for MRY with on average

26.1% followed by JER (25.7%) and smallest for GWH with

on average 2.4%. For all breeds together, the average pre-

diction error is 25.1% with an s.d. of 15.6, which means

that the average difference between the predicted content

using MIR and the reference GC values was 25.1% (nor-

malized to the mean). The average prediction error were

highest for the predicted contents of the individual FAs

cis

-16:1 and 17:0. The

, which is clearly related to the

does not show unexpected results as

is generally closer to

1 when the

is also closer to 1. With a

value of 1.55, the

Table 5

The RPD

of 11 FAs and 3 groups of FAs for different dairy

breeds

Breed

Trait (g/dl of milk) GWH MRY DF JER All breeds

4:0 2.29 1.44 2.01 2.46 2.41

6:0 3.06 2.94 2.71 3.24 3.86

8:0 2.96 3.14 1.77 3.23 3.53

10:0 2.53 2.89 1.32 2.62 2.76

12:0 1.90 1.36 1.30 2.69 1.91

14:0 2.84 3.30 3.09 3.33 3.80

cis

-14:1 1.74 1.84 1.01 0.85 1.28

16:0 2.25 1.52 2.06 1.61 2.50

cis

-16:1 0.74 0.47 0.55 1.00 0.85

17:0 0.79 0.44 0.40 0.67 0.68

18:0 1.42 0.75 0.72 0.86 1.09

SFA 8.18 14.20 12.72 6.30 11.55

SCFA 3.14 3.66 2.64 3.59 4.29

MCFA 4.16 3.19 2.59 2.80 3.87

RPD

5ratio of the standard deviation of the validation samples to the standard

error of prediction of the validation; FA5fatty acid; GWH 5Groningen White

Headed; MRY 5Meuse-Rhine-Yssel; DF 5Dutch Friesian; JER 5Jersey; SFA5the

saturated FAs 4:0 to 22:0 including iso- and ante-iso FAs; SCFA 5short-chain FAs

4:0 to 10:0; MCFA5medium-chain FAs 12:0 to 16:0.

Calibration equations with RPD

above 3.0 can be considered as good predictors

(Williams and Sobering, 1993. Journal of Near Infrared Spectroscopy 1, 25–32).

Breeds total is the RPD

across the breeds GWH, DF, MRY and JER.

Table 6

The average prediction error

of the predictions of 11 FAs and

3 groups of FAs for different dairy breeds

Breed

Trait (g/dl of milk) GWH MRY DF JER All breeds

4:0 24.6 210.7 26.1 23.3 26.4

6:0 20.4 22.9 2.6 0.5 20.1

8:0 20.8 20.4 6.7 20.4 1.5

10:0 0.7 4.9 12.1 7.5 6.3

12:0 6.6 16.8 12.4 4.3 10.3

14:0 3.3 5.1 3.2 21.4 2.6

cis

-14:1 25.5 28.3 223.0 235.9 217.9

16:0 24.5 210.4 211.3 28.5 28.8

cis

-16:1 228.3 254.4 242.5 230.4 239.5

17:0 224.6 242.5 243.5 228.6 235.1

18:0 12.1 22.9 22.1 19.9 19.4

SFA 1.2 0.8 0.9 0.6 0.9

SCFA 20.9 21.8 3.7 0.6 0.4

MCFA 21.1 25.0 26.4 25.4 24.5

Mean

23.4 26.1 24.9 25.7 25.1

s.d.

10.4 19.7 18.8 15.0 15.6

FA 5fatty acid; GWH 5Groningen White Headed; DF 5Dutch Friesian;

MRY 5Meuse-Rhine-Yssel; JER 5Jersey; SFA 5the saturated FAs 4:0 to 22:0

including iso- and ante-iso FAs; SCFA 5short-chain FAs 4:0 to 10:0;

MCFA 5medium-chain FAs 12:0 to 16:0.

The average prediction error calculated as the predicted value minus

the reference gas chromatography values and expressed as percentage of

the mean of the gas chromatography values: (average prediction error/

mean) 3100.

All breeds means the average prediction errors across all predictions for

GWH, DF, MRY and JER.

The mean and s.d. of all average prediction errors for each breed and all

breeds together.

Table 7

The slope (

) of the linear regression with the gas chroma-

tography values as dependent and the predicted values as indepen-

dent variable of 11 FAs and 3 groups of FAs for different dairy breeds

Breed

Trait (g/dl of milk) GWH MRY DF JER All breeds

4:0 0.95 0.95 0.99 1.16 1.06

6:0 0.90 0.92 0.95 1.07 0.99

8:0 0.97 0.96 1.00 1.06 0.99

10:0 1.04 1.16 1.12 1.14 1.13

12:0 1.27 1.26 1.10 1.14 1.09

14:0 0.93 1.04 1.01 0.91 0.93

cis

-14:1 1.22 1.10 0.91 1.11 0.85

16:0 0.92 0.95 1.03 1.00 0.99

cis

-16:1 0.77 0.71 0.71 0.89 0.88

17:0 0.76 0.37 0.83 0.63 0.80

18:0 1.55 0.90 0.84 0.82 0.98

SFA 0.99 1.00 1.00 1.01 1.00

SCFA 0.93 1.00 0.97 1.08 1.02

MCFA 0.96 0.97 1.01 0.99 0.97

FA 5fatty acid; GWH 5Groningen White Headed; DF 5Dutch Friesian;

MRY 5Meuse-Rhine-Yssel; JER 5Jersey; SFA 5the saturated FAs 4:0 to 22:0

including iso- and ante-iso FAs; SCFA 5short-chain FAs 4:0 to 10:0;

MCFA 5medium-chain FAs 12:0 to 16:0.

All breeds means the average prediction errors across all predictions for

GWH, DF, MRY and JER.

Maurice-Van Eijndhoven, Soyeurt, Dehareng and Calus

352

variance of the predicted content of 18:0 for GWH milk

showed the largest underestimation (Table 7). With a

value of 0.37, the variance of the predicted content of 17:0

for MRY showed the largest overestimation, which indicated

a lack of relation between the true and predicted values also

shown by the

calculated to be 0.17.

Comparing the descriptive statistics of the GC data, the FA

content in the milk of the validation data set is generally

higher than in the milk of the calibration data set. Especially

JER milk in the validation data set showed higher FA con-

tents, as the mean contents of 6:0, 8:0, 10:0, 12:0, 14:0, SFA,

SCFA and MCFA were outside the 95% conﬁdence interval of

the mean of the calibration data of the calibration data set

(i.e. larger than 2.5 times the standard deviation above the

mean contents).

The performance of the calibration equations to predict

the content of the FAs 16:0 and

cis

-16:1 is also visualized in

Figures 1 and 2. For 16:0, a clear linear pattern is shown in

Figure 1, which result in the high validation

ranging from

0.86 to 0.93. For 16:1, Figure 2 clearly shows relatively more

deviation of the predicted values. In both ﬁgures, especially

predictions for JER are located in a different direction.

Discussion

The aim of this study was to investigate the accuracy of

calibration equations based on milk samples collected from a

population with different origin in terms of country, breed

and methodology used to measure actual FA composition. In

general, FAs with higher content in milk can be predicted

more accurately than milk with a lower FA content (Soyeurt

et al.

, 2006 and 2011; Rutten

et al.

, 2009). In this study,

predictions of FA with high content in milk (.1 g/dl milk)

were also highly accurate (validation

.0.80); however,

7 of the total 11 FAs with lower content in milk (,1 g/dl

milk) were predicted to be highly accurate by means of

validation

. Differences in performance of the calibration

equations between breeds were mainly found by evaluating

the SEV and the average prediction error. Results showed on

average for GWH the smallest difference between predicted

and reference values and least variation in prediction errors,

whereas for JER on average the largest differences were

found between predicted and reference values and most

variation in prediction errors.

The RobustMilk calibration equations validated in our

study were updated versions of the calibration equations

reported in Soyeurt

et al.

(2011), in that the calibration data

set was enlarged. Despite this increase in size of the cali-

bration data set, the predictions in our study were in general

less accurate than those of Soyeurt

et al.

(2011). Comparing

both studies, the FA composition in the validation data set of

Soyeurt

et al.

(2011) was generally closer to the FA compo-

sition of the calibration data, whereas FA concentrations in

the validation data set of our study were generally higher

than those in the calibration data. This difference in range

of FA concentrations, mainly due to differences in breed, is

the most likely reason for this lower accuracy. A comparable

difference in accuracy was found by Rutten

et al.

(2009)

when predicting FA composition in winter or summer, using

a calibration equation that was based on winter samples

only. This indicates that differences in FA composition due to

differences in season (in which the feeding regime differs)

are as important as differences due to breed (Rutten

et al.

2009). When winter milk samples were used in the calibra-

tion data set to predict FA composition of summer samples,

differences in concentration ranges between the calibration

data set and the validation data set especially affected the

bias (i.e. relative difference in means; Rutten

et al.

, 2009).

In our study, the FAs that showed the largest difference

in mean between our validation and the calibration data

were not necessarily the same FAs as those that showed the

largest bias. For instance, despite a relatively small difference

Figure 1 The predicted content of the individual fatty acid 16:0 based on

mid-infrared spectometry plotted against the reference gas chromatogra-

phy values.

Figure 2 The predicted content of the individual fatty acid

cis

-16:1 based

on mid-infrared spectometry plotted against the reference gas chromato-

graphy values.

Validation of milk fatty acid prediction

353

in concentration of 14:1,

cis

-16:1, 17:0 and 18:0 between

validation and calibration data, those FAs showed the largest

bias (i.e. average prediction error and

). As

cis

-14:1, 16:1

and 17:0 are present in very low concentrations (,0.01 g/dl

milk), this is the most likely cause of their high bias. Differences

between means of validation and calibration data were

largest for the concentrations of short and medium FAs in

JER milk, which generally had a higher concentration in JER

milk compared with the other breeds. Remarkably, despite

the large difference in concentration, these FAs generally

had accurate predictions. Therefore, it seems that differences

in accuracy and bias are not only caused by differences in

concentration of the individual FAs, but perhaps also by

spectral variability of the milk samples. As indicated by

Soyeurt

et al.

(2011), adding milk samples to the calibration

data to maximize the spectral variability of the samples in

the calibration data set is an effective method to optimize

calibration equations.

The suitability of calibration equations depends on the

application of the predictions. If the primary interest is in

predicting individual FA composition, then highly accurate

and unbiased predictions are important. When the interest is

in predicting differences between individuals or populations

(e.g. for breeding purposes), accurate and unbiased predic-

tions are important; however, less accurate or biased pre-

dictions can still be suitable, especially when multiple

measurements are available per individual. Suitability of

calibration equations, which are to some extent derived

under different conditions, can be evaluated by means of an

external validation as presented in this study.

As the dairy breeding industry is interested in selecting

cows producing milk with a speciﬁc FA composition, the

suitability of the calibration equations depends on the

reduction in genetic gain when using MIR information

instead of GC information. As Rutten

et al.

(2010) found, the

possible genetic gain estimated using FA composition

determined by predictions based on MIR was almost equal

to the possible genetic gain estimated using FA composition

determined by GC, in dairy breeding schemes with progeny

testing. The latter result was reached with even moderate

and quite low validation

’s ranging from 0.53 to 0.77. The

genetic gain estimated by Rutten

et al.

(2010) assumed the

availability of information on large groups of daughters per

sire. Reaching similar gain could be difﬁcult for the Dutch

breeds in our study, as bulls in these breeds have generally

smaller daughter groups. For these Dutch breeds, therefore,

calibration equations that give highly accurate predictions

are necessary to obtain genetic gains similar to the mainstream

cattle breeds.

Conclusion

In conclusion, the RobustMilk calibration equations can be

used to predict the content of most saturated FA in milk

using MIR spectrometry for the breeds GWH, MRY, DF and

JER in the Netherlands with only a minor loss of accuracy

compared with predictions for Holstein cows.

Acknowledgments

This study was ﬁnancially supported by the Ministry of Agri-

culture, Nature and Food (Programme ‘Kennisbasis Research’,

code: KB-04-002-021 and KB-05-003-041). The 12 involved

farmers are thanked for contributing milk samples to this study.

Furthermore, the RobustMilk project team is thanked for the

use of the calibration equations. The RobustMilk project is

ﬁnancially supported by the European Commission under the

Seventh Research Framework Programme, Grant Agreement

KBBE-211708. This publication represents the views of the

authors, not the European Commission, and the Commission is

not liable for any use that may be made of the information.

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Article

Feb 2022

This study introduces the first mid-infrared (IR)–based method for determining the fatty acid composition of human milk. A representative milk lipid fraction was obtained by applying a rapid and solvent-free two-step centrifugation method. Attenuated total reflection Fourier transform infrared (ATR FT-IR) spectroscopy was applied to record absorbance spectra of pure milk fat. The obtained spectra were compared to whole human milk transmission spectra, revealing the significantly higher degree of fatty acid–related spectral features in ATR FT-IR spectra. Partial least squares (PLS)–based multivariate regression equations were established by relating ATR FT-IR spectra to fatty acid reference concentrations, obtained with gas chromatography–mass spectrometry (GC-MS). Good predictions were achieved for the most important fatty acid sum parameters: saturated fatty acids (SAT, R ² CV = 0.94), monounsaturated fatty acids (MONO, R ² CV = 0.85), polyunsaturated fatty acids (PUFA, R ² CV = 0.87), unsaturated fatty acids (UNSAT, R ² CV = 0.91), short-chain fatty acids (SCFA, R ² CV = 0.79), medium-chain fatty acids (MCFA, R ² CV = 0.97), and long-chain fatty acids (LCFA, R ² CV = 0.88). The PLS selectivity ratio (SR) was calculated in order to optimize and verify each individual calibration model. All mid-IR regions with high SR could be assigned to absorbances from fatty acids, indicating high validity of the obtained models.

Milk Fat: Origin of Fatty Acids and Influence of Nutritional Factors Thereon

Chapter

Full-text available

Apr 2007

Don L Palmquist

Ruminant milk fat is of unique composition among terrestrial mammals, due to its great diversity of component fatty acids. The diversity arises from the effects of ruminal biohydrogenation on dietary unsaturated fatty acids and the range of fatty acids synthesized de novo in the mammary gland. Forty to sixty per cent of milk fatty acids are long-chain (predominantly C18) fatty acids derived from the diet, dependent on the amount of fat in the diet. Fatty acids from C4 to C14 are synthesized de novo in the mammary gland whereas C16 arises from both diet and de novo synthesis. Milk fat is the most variable component of milk, both in concentration and composition. In dairy cattle, both the concentration and composition of milk fat are influenced by the diet. Concentration is reduced by feeding diets that contain large proportions of readily-fermentable carbohydrates (starch) and unsaturated fat. Conversely, the percentage of fat in milk can be increased by feeding rumen-inert fats. In ruminants, in contrast with non-ruminants, dietary fats have little effect on milk fat composition. Nevertheless, subtle changes in composition and manufacturing functionality can be effected by feeding different fats. Those fatty acids synthesized de novo, especially C12 to C16, and oleic acid (C18:1) show greatest variation when supplemental fats are fed. Modern developments in the manufacture of rumen-protected and rumen-inert fats, together with increased understanding of ruminal and animal lipid metabolism, provide considerable flexibility in manipulation of the composition of milk fat for specific nutritional and manufacturing needs. Future advances in the science of milk fat and nutrition will come from focusing on the unique biological properties of minor milk fatty acids arising from ruminal biohydrogenation and possibly some of de novo mammary origin.

Mid-infrared prediction of bovine milk fatty acids across multiple breeds, production systems, and countries

Article

Full-text available

Apr 2011
J DAIRY SCI

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 cross-validation 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.

Predicting bovine milk fat composition using infrared spectroscopy based on milk samples collected in winter and summer

Article

Full-text available

Dec 2009
J DAIRY SCI

It has recently been shown that Fourier transform infrared spectroscopy has potential for the prediction of detailed milk fat composition, even based on a limited number of observations. Therefore, there seems to be an opportunity for improvement by means of using more observations. The objective of this study was to verify whether the use of more data would add to the accuracy of predicting milk fat composition. In addition, the effect of season on modeling was quantified because large differences in milk fat composition between winter and summer samples exist. We concluded that the use of 3,622 observations does increase predictability of milk fat composition based on infrared spectroscopy. However, for fatty acids with low concentrations, the use of many observations does not increase predictability to a level at which application of the model becomes obvious. Furthermore, the effect of season on validation r-square was limited but was occasionally large on prediction bias. For fatty acids that show large differences in level and standard deviation between winter and summer, a representative sample that includes observations collected in various seasons is critical for unbiased prediction. This research shows that all major fatty acids, combined groups of fatty acids, and the ratio of saturated to unsaturated fatty acids can be predicted accurately.

Modifying milk composition to increase use of dairy products in healthy diets - Preface

Article

Dec 2006
ANIM FEED SCI TECH

This preface outlines the reasons for initiating the special issue, comments on the review process, provides a brief summary of the papers in the issue and discusses some implications of these papers on modifying milk composition. (c) 2006 Elsevier B.V. All rights reserved.

Comparison of Commercial Near Infrared Transmittance and Reflectance Instruments for Analysis of Whole Grains and Seeds

Article

Jan 1993

Near infrared transmittance and reflectance instruments were compared for the determination of protein, oil, moisture and some other constituents and parameters in several grains and seeds of commerce. Both approaches were comparable in accuracy and reproducibility. The importance of optimisation of the wavelength range in whole grain analysis is demonstrated for measurements in both the NIR and visible/NlR wavelength ranges. The RPD statistic, which relates the standard error of prediction to the standard deviation of the original data, is illustrated as a method for the evaluation of calibrations. The concept of monitoring the accuracy of analysis using whole grain calibrations with ground grain calibrations is introduced.

Analysis of Milk Fatty Acids by Gas-Liquid Chromatography1

Article

Mar 1962

SU)~:~C(ARY A method for the analysis of milk fatty acids, employing separation of methyl and butyl esters by gas-liquid chromatography (GLC), has been devel- oped. The methyl esters were separated on a diethylene glycol succinate (DEGS) column and the butyl esters on an Apiezon L colunm with temperature pro- gramming, in order to estimate the short-chain esters. Known mixtures of fatty acids and milk fat were analyzed. Average per cent recoveries for the short- chain fatty acids were, as butyl esters: 4:0--61, 6:0--79, 8:0--96. Recoveries for the rest of the common milk fatty acids were approximately 100%. For the analysis of milk fatty acids, butyl esters with suitable correction factors were used to determine 4:0, 6:0, and 8:0, and the rest of the acids were deter- mined as methyl esters.

Quantitative Fatty Acid Analysis of Milk Fat by Gas-Liquid Chromatography

Article

Apr 1961

L. M. Smith

A procedure was developed for quantitative analysis of the fatty acids of milk fat. Methyl esters of the acids were prepared by methanolysis, extracted with ethyl chloride, and separated by gas-liquid chromatography (GLC), with diethylene glycol succinate as liquid phase. Known mixtures of methyl esters were used in determining factors for correction of peak areas for losses from evaporation of short carbon chain esters and/or variations in relative detector response among the esters under the conditions of the actual analysis. Replicate samples of milk fat esters were analyzed and compared with data obtained by spectrophotometry and with results of others determined by ester distillation and GLC techniques. All the major and many of the known minor fatty acids of milk fat were determined. Advantages and limitations of the method are discussed.

Mid-infrared spectroscopy for food analysis: Recent new applications and relevant developments in sample presentation methods

Article

Feb 1999
TRAC-TREND ANAL CHEM

This article deals with recent developments in the use of mid-infrared (MIR) spectroscopy for the analysis of foods. It has become apparent that MIR can be used to address a wide range of issues and provide solutions for rapid analysis and on-line control. In parallel with the new applications to food, which include new qualitative and quantitative applications and discriminant (classification) methods, there have been several technical advances in other fields that are set to impact on the food sector. New applications of transmission methods are described, which have been particularly successful for the analysis of oils and fats. Despite new advances in other sampling techniques, transmission methods have been quite widely employed. Diffuse reflectance has also been used with some considerable success, with new accessory designs and applications in food authentication using chemometric methods. However, the largest number of new applications and technical developments have used attenuated total reflectance (ATR). Novel ATR cells have been designed for high-temperature, high-pressure and a range of on-line applications. ATR has been used for a wide range of analytical application. The analysis of sugars in various systems has been particularly well studied. The authors predict that the use of MIR spectroscopy is likely to continue to increase and develop in the near future.

Short communication: Milk fat composition of 4 cattle breeds in the Netherlands

Article

Feb 2011
J DAIRY SCI

Milk fatty acid (FA) composition was compared among 4 cattle breeds in the Netherlands: Dutch Friesian (DF; 47 animals/3 farms), Meuse-Rhine-Yssel (MRY; 52/3), Groningen White Headed (GWH; 45/3), and Jersey (JER; 46/3). Each cow was sampled once between December 2008 and March 2009 during the indoor housing season, and samples were analyzed using gas chromatography. Significant breed differences were found for all traits including fat and protein contents, 13 major individual FA, 9 groups of FA, and 5 indices. The saturated fatty acid proportion, which is supposed to be unfavorable for human health, was smaller for GWH (68.9%) compared with DF (74.1%), MRY (72.3%), and JER (74.3%) breeds. The proportion of conjugated linoleic acid and the unsaturation index, which are associated positively with human health, were both highest for GWH. Differences in milk fat composition can be used in strategies to breed for milk with a FA profile more favorable for human health. Our results support the relevance of safeguarding the local Dutch breeds.

The effect of the number of observations used for Fourier transform infrared model calibration for bovine milk fat composition on the estimated genetic parameters of the predicted data

Article

Oct 2010
J DAIRY SCI

Fourier transform infrared spectroscopy is a suitable method to determine bovine milk fat composition. However, the determination of fat composition by gas chromatography, required for calibration of the infrared prediction model, is expensive and labor intensive. It has recently been shown that the number of calibration samples is strongly related to the model's validation r(2) (i.e., accuracy of prediction). However, the effect of the number of calibration samples used, and therefore validation r(2), on the estimated genetic parameters of data predicted using the model needs to be established. To this end, 235 calibration data subsets of different sizes were sampled: n=100, n=250, n=500, and n=1,000 calibration samples. Subsequently, these data subsets were used to calibrate fat composition prediction models for 2 specific fatty acids: C16:0 and C18u (where u=unsaturated). Next, genetic parameters were estimated on predicted fat composition data for these fatty acids. Strong relationships between the number of calibration samples and validation r(2), as well as strong genetic correlations were found. However, the use of n=100 calibration samples resulted in a broad range of validation r(2) values and genetic correlations. Subsequent increases of the number of calibration samples resulted in narrowing patterns for validation r(2) as well as genetic correlations. The use of n=1,000 calibration samples resulted in estimated genetic correlations varying within a range of 0.10 around the average, which seems acceptable. Genetic analyses for the human health-related fatty acids C14:0, C16:0, and C18u, and the ratio of saturated fatty acids to unsaturated fatty acids showed that replacing observations on fat composition determined by gas chromatography by predictions based on infrared spectra reduced the potential genetic gain to 98, 86, 96, and 99% for the 4 fatty acid traits, respectively, in dairy breeding schemes where progeny testing is practiced. We conclude that a relatively large number of calibration samples is required to be able to obtain genetic correlations that lie within a limited range. Considering that the routine recording of infrared spectra is relatively cheap and straightforward, we concluded that this methodology provides an excellent means for the dairy industry to genetically alter milk fat composition.