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

Eijndhoven, Myrthe Maurice Van; Soyeurt, Hélène; Dehareng, Frédéric; Calus, M.P.L.

doi:10.1017/s1751731112001218

Cited by 45 publications

(55 citation statements)

References 15 publications

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“…By selecting the same number of samples according to spectral variability, adopting a mathematical pretreatment of spectral data before PLS, and selecting only a quarter of the FTIR spectrum, Soyeurt et al (2011) improved the calibration R 2 values to 0.91, 0.58, 0.92, and 0.87, and the validation R 2 values to 0.90, 0.50, 0.86, and 0.74, respectively, for the 4 FA. By applying PLS to the first derivative of spectral data of 1,236 analyzed samples to predict the amounts of the same 4 FA in milk, Maurice-Van Eijndhoven et al (2013) obtained R 2 values of 0.96, 0.80, 0.98, and 0.91 from calibration, and of 0.85 to 0.94, 0.64 to 0.80, 0.86 to 0.93, and 0.58 to 0.80 from validation, according to the breed of cow.…”

Section: Discussionmentioning

confidence: 99%

Bayesian regression models outperform partial least squares methods for predicting milk components and technological properties using infrared spectral data

Ferragina

Campos

Vázquez

et al. 2015

Journal of Dairy Science

102

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The aim of this study was to assess the performance of Bayesian models commonly used for genomic selection to predict “difficult-to-predict” dairy traits, such as milk fatty acid (FA) expressed as percentage of total fatty acids, and technological properties, such as fresh cheese yield and protein recovery, using Fourier-transform infrared (FTIR) spectral data. Our main hypothesis was that Bayesian models that can estimate shrinkage and perform variable selection may improve our ability to predict FA traits and technological traits above and beyond what can be achieved using the current calibration models (e.g., partial least squares, PLS). To this end, we assessed a series of Bayesian methods and compared their prediction performance with that of PLS. The comparison between models was done using the same sets of data (i.e., same samples, same variability, same spectral treatment) for each trait. Data consisted of 1,264 individual milk samples collected from Brown Swiss cows for which gas chromatographic FA composition, milk coagulation properties, and cheese-yield traits were available. For each sample, 2 spectra in the infrared region from 5,011 to 925 cm−1 were available and averaged before data analysis. Three Bayesian models: Bayesian ridge regression (Bayes RR), Bayes A, and Bayes B, and 2 reference models: PLS and modified PLS (MPLS) procedures, were used to calibrate equations for each of the traits. The Bayesian models used were implemented in the R package BGLR (http://cran.r-project.org/web/packages/BGLR/index.html), whereas the PLS and MPLS were those implemented in the WinISI II software (Infrasoft International LLC, State College, PA). Prediction accuracy was estimated for each trait and model using 25 replicates of a training-testing validation procedure. Compared with PLS, which is currently the most widely used calibration method, MPLS and the 3 Bayesian methods showed significantly greater prediction accuracy. Accuracy increased in moving from calibration to external validation methods, and in moving from PLS and MPLS to Bayesian methods, particularly Bayes A and Bayes B. The maximum R2 value of validation was obtained with Bayes B and Bayes A. For the FA, C10:0 (% of each FA on total FA basis) had the highest R2 (0.75, achieved with Bayes A and Bayes B), and among the technological traits, fresh cheese yield R2 of 0.82 (achieved with Bayes B). These 2 methods have proven to be useful instruments in shrinking and selecting very informative wavelengths and inferring the structure and functions of the analyzed traits. We conclude that Bayesian models are powerful tools for deriving calibration equations, and, importantly, these equations can be easily developed using existing open-source software. As part of our study, we provide scripts based on the open source R software BGLR, which can be used to train customized prediction equations for other traits or populations.

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Section: Discussionmentioning

confidence: 99%

Bayesian regression models outperform partial least squares methods for predicting milk components and technological properties using infrared spectral data

Ferragina

Campos

Vázquez

et al. 2015

Journal of Dairy Science

102

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“…Several studies have investigated the potential use of MIR spectroscopy to predict MFA composition in dairy cattle, which have been extensively reviewed by De Marchi et al In general, these studies find a clear relationship between MFA concentration (g 100 g −1 FA) and the accuracy of the MIR spectroscopy prediction models; the accuracy of the MIR spectroscopy prediction models for major MFA is higher compared with minor MFA. The accuracy of MIR spectroscopy prediction models is also higher for individual saturated fatty acids (SFA) than individual UFA.…”

Section: Mid‐infrared To Measure Milk Fatty Acidsmentioning

confidence: 99%

Prediction of methane emission from lactating dairy cows using milk fatty acids and mid-infrared spectroscopy

Gastelen

Dijkstra

2016

J. Sci. Food Agric.

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Enteric methane (CH4 ) production is among the main targets of greenhouse gas mitigation practices for the dairy industry. A simple, robust and inexpensive measurement technique applicable on a large scale to estimate CH4 emission from dairy cattle would therefore be valuable. Milk fatty acids (MFA) are related to CH4 production because of the common biochemical pathway between CH4 and fatty acids in the rumen. A summary of studies that investigated the predictive power of MFA composition for CH4 emission indicated good potential, with predictive power ranging between 47% and 95%. Until recently, gas chromatography (GC) was the principal method used to determine the MFA profile, but GC is unsuitable for routine analysis. This has led to the application of mid-infrared (MIR) spectroscopy. The major advantages of using MIR spectroscopy to predict CH4 emission include its simplicity and potential practical application at large scale. Disadvantages include the inability to predict important MFA for CH4 prediction, and the moderate predictive power for CH4 emission. It may not be sufficient to predict CH4 emission based on MIR alone. Integration with other factors, like feed intake, nutrient composition of the feed, parity, and lactation stage may improve the prediction of CH4 emission using MIR spectra. © 2016 Society of Chemical Industry.

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“…The definition of the groups of FA are given in Table 1 and some descriptive statistics of the calibration equations, which are described by Soyeurt et al (2011), are given in Table 2. More detailed descriptive statistics of the calibration equations are published by Maurice-Van Eijndhoven et al (2012) including an external validation for the MRY breed.…”

Section: Measuring Fatty Acid Compositionmentioning

confidence: 99%

Heritability of milk fat composition is considerably lower for Meuse-Rhine-Yssel compared to Holstein Friesian cattle

et al. 2015

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a b s t r a c tThe aim of this paper is to identify differences in genetic variation of fatty acid (FA) composition in milk in different breeds. Data used included Meuse-Rhine-Yssel (MRY) and Holstein Friesian (HF) cattle breeds which were raised in the Netherlands. Both populations participated in the same milk recording system, but differed in selection history, where in the MRY there has been relatively very little emphasis on selection for high-input high-output production systems compared to HF. Differences in genetic variation were investigated by estimating breed specific additive genetic variances and heritabilities for FA contents in milk of MRY and HF. Mid Infrared Spectrometry spectra were used to predict total fat percentage and detailed FA contents in milk (14 individual FA and 14 groups of FA in g of fat/dL of milk). The dataset for MRY contained 2916 records from 2049 registered cows having at least 50% genes of MRY origin and the dataset used for HF contained 155,319 records from 96,315 registered cows having at least 50% genes of HF origin. Variance components of individual FA content in milk for the different breeds were estimated using a single trait animal model. Additive genetic variances for FA produced through de novo synthesis (short chain FA, C12:0, C14:0, and partly C16:0), C14:1 c-9 and C16:1 c-9 were significantly higher (P o0.001) for HF compared to MRY. Heritabilities of the individual FA, C4:0 to C18:0, for HF ranged from 0.28 to 0.52 and for MRY from 0.17 to 0.34. Heritabilities of the individual C18 unsaturated FA for HF ranged from 0.11 to 0.34 and for MRY from 0.10 to 0.26. Although the mean content in milk for the FA C18:2 c-9, t-11 was low in both breeds, the additive genetic variance in our dataset was significantly higher for MRY (Po 0.05) compared to HF. Heritabilities of the groups of FA for HF ranged from 0.19 to 0.53 and for MRY from 0.11 to 0.28. For the majority of the FA, the additive genetic variances for HF were significantly higher compared to MRY, except for most of the poly-unsaturated FA. The results for the poly-unsaturated FA, however, may be affected by the lower accuracy of the predictions for these FA. In conclusion, our results show that the HF breed has substantially larger genetic variance for most FA compared to MRY.

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

Cited by 45 publications

References 15 publications

Bayesian regression models outperform partial least squares methods for predicting milk components and technological properties using infrared spectral data

Bayesian regression models outperform partial least squares methods for predicting milk components and technological properties using infrared spectral data

Prediction of methane emission from lactating dairy cows using milk fatty acids and mid-infrared spectroscopy

Heritability of milk fat composition is considerably lower for Meuse-Rhine-Yssel compared to Holstein Friesian cattle

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