Predicting blood β-hydroxybutyrate using milk Fourier transform infrared spectrum, milk composition, and producer-reported variables with multiple linear regression, partial least squares regression, and artificial neural network

Pralle, Ryan S.; Weigel, K.W.; White, H.M.

doi:10.3168/jds.2017-14076

Cited by 51 publications

(58 citation statements)

References 21 publications

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“…The accuracy of our MIR prediction model for serum BHB was moderate (0.48 ≤ R 2 ≤ 0.60), which was better than those reported by Belay et al (2017a) and similar to those reported by Smith et al (2016) and Pralle et al (2018). The moderate accuracy may have been in part due to the low prevalence of elevated serum BHB concentrations in our data set.…”

Section: Pls Regression Models For Predicting Serum Metabolite Concensupporting

confidence: 85%

“…It follows, therefore, that these models may be useful for identifying cows with metabolic disorders based on their milk MIR spectra. Several authors have converted MIR predictions of continuous traits, such as BHB concentration, into binary traits based on metabolic profile thresholds (Gelé et al, 2015;Pralle et al, 2018). As far as we know, ours is the first report using PLS-DA models to classify animals directly using milk MIR spectral data.…”

Section: Pls-da For Classifying Animals Based On Metabolic Profile Tementioning

confidence: 95%

“…Several authors have demonstrated that MIR spectral data can be used to screen for subclinical ketosis through identification of ketone bodies in milk (de Roos et al, 2007;van Knegsel et al, 2010;Grelet et al, 2016) and to estimate energy balance in early lactation (McParland et al, 2011). Attempts have also been made to estimate the concentration of serum biomarkers of energy balance using milk MIR spectra (Gelé et al, 2015;Belay et al, 2017a;Pralle et al, 2018).…”

Section: Introductionmentioning

confidence: 99%

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Metabolic profiling of early-lactation dairy cows using milk mid-infrared spectra

Luke

Rochfort

Wales

et al. 2019

Journal of Dairy Science

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Metabolic disorders in early lactation have negative effects on dairy cow health and farm profitability. One method for monitoring the metabolic status of cows is metabolic profiling, which uses associations between the concentrations of several metabolites in serum and the presence of metabolic disorders. In this cross-sectional study, we investigated the use of midinfrared (MIR) spectroscopy of milk for predicting the concentrations of these metabolites in serum. Between July and October 2017, serum samples were taken from 773 early-lactation Holstein Friesian cows located on 4 farms in the Gippsland region of southeastern Victoria, Australia, on the same day as milk recording. The concentrations in sera of β-hydroxybutyrate (BHB), fatty acids, urea, Ca, Mg, albumin, and globulins were measured by a commercial diagnostic laboratory. Optimal concentration ranges for each of the 7 metabolites were obtained from the literature. Animals were classified as being either affected or unaffected with metabolic disturbances based on these ranges. Milk samples were analyzed by MIR spectroscopy. The relationships between serum metabolite concentrations and MIR spectra were investigated using partial least squares regression. Partial least squares discriminant analyses (PLS-DA) were used to classify animals as being affected or not affected with metabolic disorders. Calibration equations were constructed using data from a randomly selected subset of cows (n = 579). Data from the remaining cows (n = 194) were used for validation. The coefficient of determination (R 2) of serum BHB, fatty acids, and urea predictions were 0.48, 0.61, and 0.90, respectively. Predictions of Ca, Mg, albumin, and globulin concentrations were poor (0.06 ≤ R 2 ≤ 0.17). The PLS-DA models could predict elevated fatty acid and urea concentrations with an accuracy of approximately 77 and 94%, respectively. A second independent validation data set was assembled in March 2018, comprising blood and milk samples taken from 105 autumn-calving cows of various breeds. The accuracies of BHB and fatty acid predictions were similar to those obtained using the first validation data set. The PLS-DA results were difficult to interpret due to the low prevalence of metabolic disorders in the data set. Our results demonstrate that MIR spectroscopy of milk shows promise for predicting the concentration of BHB, fatty acids, and urea in serum; however, more data are needed to improve prediction accuracies.

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Section: Pls Regression Models For Predicting Serum Metabolite Concensupporting

confidence: 85%

Section: Pls-da For Classifying Animals Based On Metabolic Profile Tementioning

confidence: 95%

Section: Introductionmentioning

confidence: 99%

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Metabolic profiling of early-lactation dairy cows using milk mid-infrared spectra

Luke

Rochfort

Wales

et al. 2019

Journal of Dairy Science

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“…The hypothesis behind this was that cows in the good and poor groups are more likely to differ in their metabolic status, which would result in different reproductive performance (Oikonomou et al, 2008;Pryce et al, 2016). Such differences in metabolic status are expected to be captured by MIR spectra (Belay et al, 2017;Grelet et al, 2018;Pralle et al, 2018;Luke et al, 2019). The metabolic characteristics of the cows in the average group were hypothesized to be similar to those of the other 2 groups, which consequently makes them difficult to differentiate.…”

Section: Resultsmentioning

confidence: 99%

Classifying the fertility of dairy cows using milk mid-infrared spectroscopy

Bonfatti

Luke

et al. 2019

Journal of Dairy Science

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The objective of this study was to investigate the potential of milk mid-infrared (MIR) spectroscopy, MIR-derived traits including milk composition, milk fatty acids, and blood metabolic profiles (fatty acids, β-hydroxybutyrate, and urea), and other on-farm data for discriminating cows of good versus poor likelihood of conception to first insemination (i.e., pregnant vs. open). A total of 6,488 spectral and milk production records of 2,987 cows from 19 commercial dairy herds across 3 Australian states were used. Seven models, comprising different explanatory variables, were examined. Model 1 included milk production; concentrations of fat, protein, and lactose; somatic cell count; age at calving; days in milk at herd test; and days from calving to insemination. Model 2 included, in addition to the variables in model 1, milk fatty acids and blood metabolic profiles. The MIR spectrum collected before first insemination was added to model 2 to form model 3. Fat, protein, and lactose percentages, milk fatty acids, and blood metabolic profiles were removed from model 3 to create model 4. Model 5 and model 6 comprised model 4 and either fertility genomic estimated breeding value or principal components obtained from a genomic relationship matrix derived using animal genotypes, respectively. In model 7, all previously described sources of information, but not MIR-derived traits, were used. The models were developed using partial least squares discriminant analysis. The performance of each model was evaluated in 2 ways: 10-fold random cross-validation and herd-by-herd external validation. The accuracy measures were sensitivity (i.e., the proportion of pregnant cows that were correctly classified), specificity (i.e., the proportion of open cows that were correctly classified), and area under the curve (AUC) for the receiver operating curve. The results showed that in all models, prediction accuracy obtained through 10fold random cross-validation was higher than that of herd-by-herd external validation, with the difference in AUC ranging between 0.01 and 0.09. In the herd-byherd external validation, using basic on-farm information (model 1) was not sufficient to classify good-and poor-fertility cows; the sensitivity, specificity, and AUC were around 0.66. Compared with model 1, adding milk fatty acids and blood metabolic profiles (model 2) increased the sensitivity, specificity, and AUC by 0.01, 0.02, and 0.02 unit, respectively (i.e., 0.65, 0.63, and 0.678). Incorporating MIR spectra into model 2 resulted in sensitivity, specificity, and AUC values of 0.73, 0.63, and 0.72, respectively (model 3). The comparable prediction accuracies observed for models 3 and 4 mean that useful information from MIR-derived traits is already included in the spectra. Adding the fertility genomic estimated breeding value and animal genotypes (model 7) produced the highest prediction accuracy, with sensitivity, specificity, and AUC values of 0.75, 0.66, and 0.75, respectively. However, removing either the fertility estimated breeding value or ...

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“…The KBR was ground into a powder, and then mixed with Quercus dentata Thunb leaf extract to a proportion of 100:1, respectively. After grinding on a tablet [11]. (Au query: please rewrite the mention of the tablet into the sentence or place within the paragraph where it belongs.…”

Section: The Ft-ir Analysismentioning

confidence: 99%

Extracts of Quercus dentata leaf

Peng

Sun

Wang³

et al. 2020

Therm sci

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Along with the development of our economic society and the growing demand for wood, the value of the use of Quercus dentata Thunb has also increased year by year. However, the traditional trees used are resources that are cut down, and development primarily uses only the branches, while ignoring the value in using the rest of the trees. Thiscauses a waste of resources. In response, this study uses benzene, an alcohol found in Quercus dentata Thunb leaf extract. Fourier transform infrared spectroscopy (FT-IR) and gas chromatography-mass spectrometry (GC-MS) were used for catalytic cracking products, and the efficient extraction of pyrolysis products were studied. Analysis shows that Quercus dentata Thunb leaf extract species contain a variety of biological active ingredients; and the ingredients in pharmaceutical and chemical production have very high value. For example, the Quercus dentata Thunb leaf of benzyl alcohol extract, Lupeol, has significant anti-cancer, anti-oxidation, and anti-inflammatory effects. The analysis of the chemical composition of the Quercus dentata Thunb leaf provides a new theoretical basis.

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Predicting blood β-hydroxybutyrate using milk Fourier transform infrared spectrum, milk composition, and producer-reported variables with multiple linear regression, partial least squares regression, and artificial neural network

Cited by 51 publications

References 21 publications

Metabolic profiling of early-lactation dairy cows using milk mid-infrared spectra

Metabolic profiling of early-lactation dairy cows using milk mid-infrared spectra

Classifying the fertility of dairy cows using milk mid-infrared spectroscopy

Extracts of Quercus dentata leaf

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