Development of Fourier transform mid-infrared calibrations to predict acetone, β-hydroxybutyrate, and citrate contents in bovine milk through a European dairy network

Fourier transform mid-infrared (FT-MIR) spectra of milk are commonly used for phenotyping of traits of interest through links developed between the traits and milk FT-MIR spectra. Predicted traits are then used in genetic analysis for ultimate phenotypic prediction using a single-trait mixed model that account for cows' circumstances at a given test day. Here, this approach is referred to as indirect prediction (IP). Alternatively, FT-MIR spectral variable can be kept multivariate in the form of factor scores in REML and BLUP analyses. These BLUP predictions, including phenotype (predicted factor scores), were converted to single-trait through calibration outputs; this method is referred to as direct prediction (DP). The main aim of this study was to verify whether mixed modeling of milk spectra in the form of factors scores (DP) gives better prediction of blood β-hydroxybutyrate (BHB) than the univariate approach (IP). Models to predict blood BHB from milk spectra were also developed. Two data sets that contained milk FT-MIR spectra and other information on Polish dairy cattle were used in this study. Data set 1 (n = 826) also contained BHB measured in blood samples, whereas data set 2 (n = 158,028) did not contain measured blood values. Part of data set 1 was used to calibrate a prediction model (n = 496) and the remaining part of data set 1 (n = 330) was used to validate the calibration models, as well as to evaluate the DP and IP approaches. Dimensions of FT-MIR spectra in data set 2 were reduced either into 5 or 10 factor scores (DP) or into a single trait (IP) with calibration outputs. The REML estimates for these factor scores were found using WOMBAT. The BLUP values and predicted BHB for observations in the validation set were computed using the REML estimates. Blood BHB predicted from milk FT-MIR spectra by both approaches were regressed on reference blood BHB that had not been used in the model development. Coefficients of determination in cross-validation for untransformed blood BHB were from 0.21 to 0.32, whereas that for the log-transformed BHB were from 0.31 to 0.38. The corresponding estimates in validation were from 0.29 to 0.37 and 0.21 to 0.43, respectively, for untransformed and logarithmic BHB. Contrary to expectation, slightly better predictions of BHB were found when univariate variance structure was used (IP) than when multivariate covariance structures were used (DP). Conclusive remarks on the importance of keeping spectral data in multivariate form for prediction of phenotypes may be found in data sets where the trait of interest has strong relationships with spectral variables.

Section: Multivariate Calibration Modelscontrasting

confidence: 53%

Section: Multivariate Calibration Modelsmentioning

confidence: 99%

An attempt at predicting blood β-hydroxybutyrate from Fourier-transform mid-infrared spectra of milk using multivariate mixed models in Polish dairy cattle

Belay

Dagnachew

Kowalski

et al. 2017

“…Routine measurements of ketone bodies in milk can be done by Fourier transform mid-infrared (FT-MIR) spectrometer analysis of milk samples at test-days (de Roos et al, 2007;van der Drift et al, 2012a;Grelet et al, 2016). Those previous studies agreed that FT-MIR predicted milk ketone bodies adequately and that FT-MIR might be useful for ketosis-screening purposes.…”

Section: Introductionmentioning

confidence: 83%

Genetic parameters of blood β-hydroxybutyrate predicted from milk infrared spectra and clinical ketosis, and their associations with milk production traits in Norwegian Red cows

Belay

Svendsen²,

Kowalski

et al. 2017

The aim of this study was to estimate genetic parameters for blood β-hydroxybutyrate (BHB) predicted from milk spectra and for clinical ketosis (KET), and to examine genetic association of blood BHB with KET and milk production traits (milk, fat, protein, and lactose yields, and milk fat, protein, and lactose contents). Data on milk traits, KET, and milk spectra were obtained from the Norwegian Dairy Herd Recording System with legal permission from TINE SA (Ås, Norway), the Norwegian Dairy Association that manages the central database. Data recorded up to 120 d after calving were considered. Blood BHB was predicted from milk spectra using a calibration model developed based on milk spectra and blood BHB measured in Polish dairy cows. The predicted blood BHB was grouped based on days in milk into 4 groups and each group was considered as a trait. The milk components for testday milk samples were obtained by Fourier transform mid-infrared spectrometer with previously developed calibration equations from Foss (Hillerød, Denmark). Veterinarian-recorded KET data within 15 d before calving to 120 d after calving were used. Data were analyzed using univariate or bivariate linear animal models. Heritability estimates for predicted blood BHB at different stages of lactation were moderate, ranging from 0.250 to 0.365. Heritability estimate for KET from univariate analysis was 0.078, and the corresponding average estimate from bivariate analysis with BHB or milk production traits was 0.002. Genetic correlations between BHB traits were higher for adjacent lactation intervals and decreased as intervals were further apart. Predicted blood BHB at first test day was moderately genetically correlated with KET (0.469) and milk traits (ranged from −0.367 with protein content to 0.277 with milk yield), except for milk fat content from across lactation stages that had near zero genetic correlation with BHB (0.033). These genetic correlations indicate that a lower BHB is genetically associated with higher milk protein and lactose contents, but with lower yields of milk, fat, protein, and lactose, and with lower frequency of KET. Estimates of genetic correlation of KET with milk production traits were from −0.333 (with protein content) to 0.178 (with milk yield). Blood BHB can routinely be predicted from milk spectra analyzed from test-day milk samples, and thereby provides a practical alternative for selecting cows with lower susceptibility to ketosis, even though the correlations are moderate.

“…All the spectra were transformed in absorbance and interpolated to match the wave number range of the master to observe differences from spectral response only. A PCA was performed on spectra after a first derivative with a gap of 5 and using 212 selected wave numbers, from 968.1 to 1,577.5 cm −1 , 1,731.8 to 1,762.6 cm −1 , 1,781.9 to 1,808.9 cm −1 , and 2,831.0 to 2,966.0 cm −1 (Grelet et al, 2016). Based on the second PCA, the improvement of spectral reproducibility was quantitatively assessed by comparing the global Mahalanobis distances (GH) of the slaves from the master before and after standardization.…”

Section: Spectral Reproducibility Within the Networkmentioning

confidence: 99%

“…Equations based on the full spectrum have been developed for the determination of fine milk components such as fatty acid profiles (Soyeurt et al, 2006;Rutten et al, 2009), protein composition (Bonfatti et al, 2011), minerals (Soyeurt et al, 2009), ketone bodies (van Knegsel et al, 2010), citrate (Grelet et al, 2016), and lactoferrin (Soyeurt et al, 2007). Other studies have focused on FT-MIR spectra to build equations predicting technological properties of milk such as milk acidity ), ability to coagulate, firmness of curd, or cheese yield (Dal Zotto et al, 2008;Colinet et al, 2015).…”

Section: Introductionmentioning

confidence: 99%

Standardization of milk mid-infrared spectrometers for the transfer and use of multiple models

Grelet

Pierna

Dardenne

et al. 2017

Self Cite

An increasing number of models are being developed to provide information from milk Fourier transform mid-infrared (FT-MIR) spectra on fine milk composition, technological properties of milk, or even cows' physiological status. In this context, and to take advantage of these existing models, the purpose of this work was to evaluate whether a spectral standardization method can enable the use of multiple equations within a network of different FT-MIR spectrometers. The piecewise direct standardization method was used, matching "slave" instruments to a common reference, the "master." The effect of standardization on network reproducibility was assessed on 66 instruments from 3 different brands by comparing the spectral variability of the slaves and the master with and without standardization. With standardization, the global Mahalanobis distance from the slave spectra to the master spectra was reduced on average from 2,655.9 to 14.3, representing a significant reduction of noninformative spectral variability. The transfer of models from instrument to instrument was tested using 3 FT-MIR models predicting (1) the quantity of daily methane emitted by dairy cows, (2) the concentration of polyunsaturated fatty acids in milk, and (3) the fresh cheese yield. The differences, in terms of root mean squared error, between master predictions and slave predictions were reduced after standardization on average from 103 to 17 g/d, from 0.0315 to 0.0045 g/100 mL of milk, and from 2.55 to 0.49 g of curd/100 g of milk, respectively. For all the models, standard deviations of predictions among all the instruments were also reduced by 5.11 times for methane, 5.01 times for polyunsaturated fatty acids, and 7.05 times for fresh cheese yield, showing an improvement of prediction reproducibility within the network. Regarding the results obtained, spectral standardization allows the transfer and use of multiple models on all instruments as well as the improvement of spectral and prediction reproducibility within the network. The method makes the models universal, thereby offering opportunities for data exchange and the creation and use of common robust models at an international level to provide more information to the dairy sector from direct analysis of milk.