Genetic analysis on infrared-predicted milk minerals for Danish dairy cattle

Zaalberg, R.M.; Poulsen, Nina Aagaard; Bovenhuis, Henk; Sehested, Jakob; Larsen, Lotte Bach; Buitenhuis, Bart

doi:10.3168/jds.2020-19638

Cited by 19 publications

(6 citation statements)

References 46 publications

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“…Soyeurt et al (2011) suggested that models with R 2 > 0.75 might be utilized for animal breeding. However, Zaalberg et al (2021) used prediction models with R 2 > 0.6 for mineral elements in animal breeding [ 31 ]. Cecchinato et al (2009) showed low R 2 values for curd characteristics predicted by MIRS, but they found high genetic correlations between the measured values and the predicted values [ 32 ].…”

Section: Discussionmentioning

confidence: 99%

Predictions of Milk Fatty Acid Contents by Mid-Infrared Spectroscopy in Chinese Holstein Cows

Zhao

Song

Zhang³

et al. 2023

Molecules

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Genetic improvement of milk fatty acid content traits in dairy cattle is of great significance. However, chromatography-based methods to measure milk fatty acid content have several disadvantages. Thus, quick and accurate predictions of various milk fatty acid contents based on the mid-infrared spectrum (MIRS) from dairy herd improvement (DHI) data are essential and meaningful to expand the amount of phenotypic data available. In this study, 24 kinds of milk fatty acid concentrations were measured from the milk samples of 336 Holstein cows in Shandong Province, China, using the gas chromatography (GC) technique, which simultaneously produced MIRS values for the prediction of fatty acids. After quantification by the GC technique, milk fatty acid contents expressed as g/100 g of milk (milk-basis) and g/100 g of fat (fat-basis) were processed by five spectral pre-processing algorithms: first-order derivative (DER1), second-order derivative (DER2), multiple scattering correction (MSC), standard normal transform (SNV), and Savitzky–Golsy convolution smoothing (SG), and four regression models: random forest regression (RFR), partial least square regression (PLSR), least absolute shrinkage and selection operator regression (LassoR), and ridge regression (RidgeR). Two ranges of wavebands (4000~400 cm−1 and 3017~2823 cm−1/1805~1734 cm−1) were also used in the above analysis. The prediction accuracy was evaluated using a 10-fold cross validation procedure, with the ratio of the training set and the test set as 3:1, where the determination coefficient (R2) and residual predictive deviation (RPD) were used for evaluations. The results showed that 17 out of 31 milk fatty acids were accurately predicted using MIRS, with RPD values higher than 2 and R2 values higher than 0.75. In addition, 16 out of 31 fatty acids were accurately predicted by RFR, indicating that the ensemble learning model potentially resulted in a higher prediction accuracy. Meanwhile, DER1, DER2 and SG pre-processing algorithms led to high prediction accuracy for most fatty acids. In summary, these results imply that the application of MIRS to predict the fatty acid contents of milk is feasible.

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Section: Discussionmentioning

confidence: 99%

Predictions of Milk Fatty Acid Contents by Mid-Infrared Spectroscopy in Chinese Holstein Cows

Zhao

Song

Zhang³

et al. 2023

Molecules

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“…Another difference is clearly exposed by Zaalberg et al [26], and a mean difference is observed for Danish Holstein with −4.09%, 5.07%, 8.08%, −29.32%, and −5.49% and for Danish Jersey with 12.35%, 23.71%, 17.91%, −13.61%, and −14.49% for Na, Ca, Mg, P, and K, respectively, in comparison to our data.…”

Section: Reference Datasetmentioning

confidence: 92%

“…Consequently, there is a need to assess the mineral profile in milk through costeffective and large-scale methods, such as milk MIR spectrometry. From 2009 and the first study conducted by Soyeurt et al [19], numerous studies were carried out to develop MIR models predicting milk minerals [20][21][22][23][24][25][26]. For wide, large-scale use by dairy herd improvement (DHI) centers and dairy industries, such models need to be robust to provide accurate and reproducible predictions in various conditions.…”

Section: Introductionmentioning

confidence: 99%

“…For this reason, the first objective and the novelty of this work is to develop MIR models predicting the mineral content of milk from a large and variable dataset, including records coming from several countries, breeds, seasons, diets, and MIR spectrometers. No models presented in the literature [20][21][22][23][24][25][26] exhibit a multi-country approach, and the models presented could allow a wide range of utilization for the farmer and could be used for all the countries present in the calibration set. The second objective was to test the models on a country-independent validation dataset and to evaluate the impact of including local samples in the calibration dataset.…”

Section: Introductionmentioning

confidence: 99%

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Multiple Breeds and Countries’ Predictions of Mineral Contents in Milk from Milk Mid-Infrared Spectrometry

Christophe

Grelet

Bertozzi³

et al. 2021

Foods

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Measuring the mineral composition of milk is of major interest in the dairy sector. This study aims to develop and validate robust multi-breed and multi-country models predicting the major minerals through milk mid-infrared spectrometry using partial least square regressions. A total of 1281 samples coming from five countries were analyzed to obtain spectra and in ICP-AES to measure the mineral reference contents. Models were built from records coming from four countries (n = 1181) and validated using records from the fifth country, Austria (n = 100). The importance of including local samples was tested by integrating 30 Austrian samples in the model while validating with the remaining 70 samples. The best performances were achieved using this second set of models, confirming the need to cover the spectral variability of a country before making a prediction. Validation root mean square errors were 54.56, 63.60, 7.30, 59.87, and 152.89 mg/kg for Na, Ca, Mg, P, and K, respectively. The built models were applied on the Walloon milk recording large-scale spectral database, including 3,510,077. The large-scale predictions on this dairy herd improvement database provide new insight regarding the minerals’ variability in the population, as well as the effect of parity, stage of lactation, breeds, and seasons.

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“…This possible threat represents a restriction for the estimation of accurate genetic parameters for milk minerals, as well as being able to obtain high accuracy estimates of genetic merit for individual animals (Visentin et al ., 2019). Therefore, infrared prediction of milk minerals could be a very important and cheap addition to the collection of milk traits that are presently accessible for farm management and breeding programs (Zaalberg et al ., 2021).…”

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confidence: 99%

A meta-analysis of genetic parameter estimates for milk and serum minerals in dairy cows

Hossein-Zadeh

2022

Journal of Dairy Research

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This study aimed to conduct a meta-analysis based on a random-effects model to combine different published heritability estimates and genetic correlations for milk and serum minerals in dairy cows. In total, 59 heritability and 25 genetic correlation estimates from 12 articles published between 2009 and 2021 were used. The heritability estimates for milk macro-minerals were moderate to high and ranged from 0.311 (for Na) to 0.420 (for Ca). On the other hand, milk micro-minerals had lower heritabilities with a range from 0.013 (for Fe) to 0.373 (for Zn). The heritability estimates for serum macro-minerals were generally low and varied from 0.126 (for K) to 0.206 (for Mg). The estimates of genetic correlation between milk macro-minerals varied from −0.024 (between Na and K) to 0.625 (between Mg and P). The genetic correlations of milk Ca and P with milk yield were −0.171 and −0.211, respectively. The estimates of genetic parameters reported in this meta-analysis study are appropriate to utilize in breeding plans when valid estimates are not available for milk minerals in dairy cow populations.

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Genetic analysis on infrared-predicted milk minerals for Danish dairy cattle

Cited by 19 publications

References 46 publications

Predictions of Milk Fatty Acid Contents by Mid-Infrared Spectroscopy in Chinese Holstein Cows

Predictions of Milk Fatty Acid Contents by Mid-Infrared Spectroscopy in Chinese Holstein Cows

Multiple Breeds and Countries’ Predictions of Mineral Contents in Milk from Milk Mid-Infrared Spectrometry

A meta-analysis of genetic parameter estimates for milk and serum minerals in dairy cows

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