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1.
Res Vet Sci ; 126: 192-198, 2019 Oct.
Artigo em Inglês | MEDLINE | ID: mdl-31539796

RESUMO

The objective of the study was to (1) characterize and compare the chemical composition at the surface, subsurface and in the bulk of thin plastic films used for portosystemic shunt attenuation in their native state and after plasma exposure. (2) Assess the presence, concentration and location of irritant compounds (e.g dicetyl phosphate) within the films. Attenuated Total Reflectance Infrared Spectroscopy (ATR-IR), X-ray Photoelectron Spectroscopy (XPS) and dynamic Time-of-Flight Secondary Ion Mass Spectrometry (ToF-SIMS) were used to analyze thirteen thin plastic films. Sample thickness was visualized and measured using Scanning Electron Microscopy (SEM). Sample thicknesses were compared using a one-way ANOVA. XPS reported low phosphorous concentrations (surrogate marker of dicetyl phosphate) between 0.01 and 0.19% wt at the sample surfaces (top 10 nm). There were significant differences between film thicknesses (P < .001) observed by SEM. The ATR-IR and ToF-SIMS identified four distinct surface and bulk chemical profiles: 1) Cellophane, 2) Polypropylene, 3) Modified Cellophane, and 4) Unique. Following plasma immersion for 6 weeks, samples showed little change in film thickness or chemical composition. This study confirmed that films used to attenuate portosystemic shunts were commonly not pure cellophane, with significant variations in surface and bulk chemistry. Suspected irritant compounds were not readily identifiable in significant proportions. Pronounced variability existed in both the thickness and chemical composition of these films (surface vs. bulk). The present findings lead to a legitimate question about the reproducibility of shunt occlusion when using thin plastic films from different origins.


Assuntos
Plásticos/análise , Plásticos/química , Derivação Portossistêmica Cirúrgica/veterinária , Animais , Gatos , Cães , Microscopia Eletrônica de Varredura/veterinária , Espectroscopia Fotoeletrônica/veterinária , Derivação Portossistêmica Cirúrgica/estatística & dados numéricos , Reprodutibilidade dos Testes , Espectrometria de Massa de Íon Secundário/veterinária , Espectrofotometria Infravermelho/veterinária , Propriedades de Superfície
2.
J Dairy Sci ; 102(11): 10460-10470, 2019 Nov.
Artigo em Inglês | MEDLINE | ID: mdl-31495611

RESUMO

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, ß-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 10-fold 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-by-herd 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 animal genotype from model 7 resulted in a reduction of the prediction accuracy of only 0.01 and 0.02, respectively. In conclusion, this study indicates that MIR and other on-farm data could be used to classify cows of good and poor likelihood of conception with promising accuracy.


Assuntos
Bovinos/fisiologia , Fertilidade , Leite/diagnóstico por imagem , Ácido 3-Hidroxibutírico/sangue , Animais , Área Sob a Curva , Austrália , Ácidos Graxos/análise , Feminino , Glicolipídeos/análise , Glicoproteínas/análise , Inseminação , Lactação , Lactose/análise , Análise dos Mínimos Quadrados , Leite/química , Proteínas do Leite/análise , Valor Preditivo dos Testes , Gravidez , Sensibilidade e Especificidade , Espectrofotometria Infravermelho/veterinária , Ureia/sangue
3.
J Dairy Sci ; 102(10): 8907-8918, 2019 Oct.
Artigo em Inglês | MEDLINE | ID: mdl-31351717

RESUMO

The objective of this study was to compare mid-infrared reflectance spectroscopy (MIRS) analysis of milk and near-infrared reflectance spectroscopy (NIRS) analysis of feces with regard to their ability to predict the dry matter intake (DMI) of lactating grazing dairy cows. A data set comprising 1,074 records of DMI from 457 cows was available for analysis. Linear regression and partial least squares regression were used to develop the equations using the following variables: (1) milk yield (MY), fat percentage, protein percentage, body weight (BW), stage of lactation (SOL), and parity (benchmark equation); (2) MIRS wavelengths; (3) MIRS wavelengths, MY, fat percentage, protein percentage, BW, SOL, and parity; (4) NIRS wavelengths; (5) NIRS wavelengths, MY, fat percentage, protein percentage, BW, SOL, and parity; (6) MIRS and NIRS wavelengths; and (7) MIRS wavelengths, NIRS wavelengths, MY, fat percentage, protein percentage, BW, SOL, and parity. The equations were validated both within herd using animals from similar experiments and across herds using animals from independent experiments. The accuracy of equations was greater for within-herd validation compared with across-herds validation. Across-herds validation was deemed the more suitable method to assess equations for robustness and real-world application. The benchmark equation was more accurate [coefficient of determination (R2) = 0.60; root mean squared error (RMSE) = 1.68 kg] than MIRS alone (R2 = 0.30; RMSE = 2.23 kg) or NIRS alone (R2 = 0.16; RMSE = 2.43 kg). The combination of the benchmark equation with MIRS (R2 = 0.64; RMSE = 1.59 kg) resulted in slightly superior fitting statistics compared with the benchmark equation alone. The combination of the benchmark equation with NIRS (R2 = 0.58; RMSE = 1.71 kg) did not result in a more accurate prediction equation than the benchmark equation. The combination of MIRS and NIRS wavelengths resulted in superior fitting statistics compared with either method alone (R2 = 0.36; RMSE = 2.15 kg). The combination of the benchmark equation and MIRS and NIRS wavelengths resulted in the most accurate equation (R2 = 0.68; RMSE = 1.52 kg). A further analysis demonstrated that Holstein-Friesian cows could predict the DMI of Jersey × Holstein-Friesian crossbred cows using both MIRS and NIRS. Similarly, the Jersey × Holstein-Friesian animals could predict the DMI of Holstein-Friesian cows using both MIRS and NIRS. The equations developed in this study have the capacity to predict DMI of grazing dairy cows. From a practicality perspective, MIRS in combination with variables in the benchmark equation is the most suitable equation because MIRS is currently used on all milk-recorded milk samples from dairy cows.


Assuntos
Bovinos , Dieta/veterinária , Herbivoria , Espectrofotometria Infravermelho/veterinária , Animais , Peso Corporal , Ingestão de Alimentos , Fezes/química , Feminino , Lactação , Análise dos Mínimos Quadrados , Modelos Lineares , Leite , Gravidez , Espectrofotometria Infravermelho/métodos
4.
J Dairy Sci ; 102(10): 9512-9517, 2019 Oct.
Artigo em Inglês | MEDLINE | ID: mdl-31351724

RESUMO

This study aimed to compare measurements of methane (CH4) and carbon dioxide (CO2) concentrations in the breath of dairy cows kept in commercial conditions using the Fourier-transform infrared spectroscopy (FTIR) and nondispersive infrared spectroscopy (NDIR) methods. The measurement systems were installed in an automated milking system. Measurements were carried out for 5 d using both systems during milkings. The measurements were averaged per milking, giving 467 observations of CH4 and CO2 concentrations of 44 Holstein Friesian cows. The Pearson correlation between observations from the 2 systems was 0.86 for CH4, 0.84 for CO2, and 0.88 for their ratio. The repeatability of FTIR (0.53 for CH4, 0.57 for CO2, and 0.28 for their ratio) was somewhat higher than that of NDIR (0.57 for CH4, 0.47 for CO2, and 0.25 for their ratio). The coefficient of individual agreement was 0.98 for CH4, 0.89 for CO2, and 0.89 for their ratio; the concordance correlation coefficient was 0.48 for both gases and 0.24 for their ratio. We showed that FTIR and NDIR give similar results in commercial farm conditions. They can therefore be used interchangeably to generate a larger data set, which could then be further used for genetic evaluation.


Assuntos
Dióxido de Carbono/análise , Bovinos/fisiologia , Metano/análise , Leite/metabolismo , Espectrofotometria Infravermelho/veterinária , Espectroscopia de Infravermelho com Transformada de Fourier/veterinária , Animais , Feminino
5.
J Dairy Sci ; 102(8): 6943-6958, 2019 Aug.
Artigo em Inglês | MEDLINE | ID: mdl-31178172

RESUMO

Assessing the cheese-making properties (CMP) of milks with a rapid and cost-effective method is of particular interest for the Protected Designation of Origin cheese sector. The aims of this study were to evaluate the potential of mid-infrared (MIR) spectra to estimate coagulation and acidification properties, as well as curd yield (CY) traits of Montbéliarde cow milk. Samples from 250 cows were collected in 216 commercial herds in Franche-Comté with the objectives to maximize the genetic diversity as well as the variation in milk composition. All coagulation and CY traits showed high variability (10 to 43%). Reference analyses performed for soft (SC) and pressed cooked (PCC) cheese technology were matched with MIR spectra. Prediction models were built on 446 informative wavelengths not tainted by the water absorbance, using different approaches such as partial least squares (PLS), uninformative variable elimination PLS, random forest PLS, Bayes A, Bayes B, Bayes C, and Bayes RR. We assessed equation performances for a set of 20 CMP traits (coagulation: 5 for SC and 4 for PCC; acidification: 5 for SC and 3 for PCC; laboratory CY: 3) by comparing prediction accuracies based on cross-validation. Overall, variable selection before PLS did not significantly improve the performances of the PLS regression, the prediction differences between Bayesian methods were negligible, and PLS models always outperformed Bayesian models. This was likely a result of the prior use of informative wavelengths of the MIR spectra. The best accuracies were obtained for curd yields expressed in dry matter (CYDM) or fresh (CYFRESH) and for coagulation traits (curd firmness for PCC and SC) using the PLS regression. Prediction models of other CMP traits were moderately to poorly accurate. Whatever the prediction methodology, the best results were always obtained for CY traits, probably because these traits are closely related to milk composition. The CYDM predictions showed coefficient of determination (R2) values up to 0.92 and 0.87, and RSy,x values of 3 and 4% for PLS and Bayes regressions, respectively. Finally, we divided the data set into calibration (2/3) and validation (1/3) sets and developed prediction models in external validation using PLS regression only. In conclusion, we confirmed, in the validation set, an excellent prediction for CYDM [R2 = 0.91, ratio of performance to deviation (RPD) = 3.39] and a very good prediction for CYFRESH (R2 = 0.84, RPD = 2.49), adequate for analytical purposes. We also obtained good results for both PCC and SC curd firmness traits (R2 ≥ 0.70, RPD ≥1.8), which enable quantitative prediction.


Assuntos
Bovinos/metabolismo , Queijo/análise , Leite/química , Animais , Teorema de Bayes , Calibragem , Feminino , França , Análise dos Mínimos Quadrados , Leite/metabolismo , Fenótipo , Espectrofotometria Infravermelho/veterinária
6.
J Dairy Sci ; 102(7): 6288-6295, 2019 Jul.
Artigo em Inglês | MEDLINE | ID: mdl-31056328

RESUMO

Because of the environmental impact of methane (CH4), it is of great interest to reduce CH4 emission of dairy cattle and selective breeding might contribute to this. However, this approach requires a rapid and inexpensive measurement technique that can be used to quantify CH4 emission for a large number of individual dairy cows. Milk infrared (IR) spectroscopy has been proposed as a predictor for CH4 emission. In this study, we investigated the feasibility of milk IR spectra to predict breath sensor-measured CH4 of 801 dairy cows on 10 commercial farms. To evaluate the prediction equation, we used random and block cross validation. Using random cross validation, we found a validation coefficient of determination (R2val) of 0.49, which suggests that milk IR spectra are informative in predicting CH4 emission. However, based on block cross validation, with farms as blocks, a negligible R2val of 0.01 was obtained, indicating that milk IR spectra cannot be used to predict CH4 emission. Random cross validation thus results in an overoptimistic view of the ability of milk IR spectra to predict CH4 emission of dairy cows. The difference between the validation strategies could be due to the confounding of farm and date of milk IR analysis, which introduces a correlation between batch effects on the IR analyses and farm-average CH4. Breath sensor-measured CH4 is strongly influenced by farm-specific conditions, which magnifies the problem. Milk IR wavenumbers from water absorption regions, which are generally considered uninformative, showed moderate accuracy (R2val = 0.25) when based on random cross validation, but not when based on block cross validation (R2val = 0.03). These results indicate, therefore, that in the current study, random cross validation results in an overoptimistic view on the ability of milk IR spectra to predict CH4 emission. We suggest prediction based on wavenumbers from water absorption regions as a negative control to identify potential dependence structures in the data.


Assuntos
Bovinos/metabolismo , Metano/química , Leite/química , Espectrofotometria Infravermelho/métodos , Animais , Feminino , Lactação , Metano/metabolismo , Leite/metabolismo , Seleção Artificial , Espectrofotometria Infravermelho/veterinária
7.
Prev Vet Med ; 164: 72-77, 2019 Mar 01.
Artigo em Inglês | MEDLINE | ID: mdl-30771896

RESUMO

Accurate diagnosis of failure of transfer of passive immunity (FTPI) in newborn calves is an essential component of dairy farm management plan. Several methods (direct and indirect) are available for diagnosis of FTPI in dairy calves. However, the indirect methods offer an advantage over the direct methods in not requiring an experienced veterinarian, rapid, cost efficient and can be performed under field-setting. The objective of this study was to estimate the diagnostic performance of radial immunodiffusion (RID) assay, transmission infrared (TIR) spectroscopy and digital Brix refractometer for diagnosis of FTPI in dairy calves using latent class models at four cut-off values of digital Brix refractometer. Holstein calves (n = 691) from 40 commercial dairy farms in the four Atlantic Canada provinces were blood-sampled and tested for detection of FTPI. Results showed that the number of calves with FTPI was 253 (36.6%) by RID, 194 (28.1%) by TIR and 204 (29.5%) by Brix refractometer at cut-off value of 8.2%. Estimates of SeRID was higher than SeTIR and SeBrix, at all Brix refractometer cut-offs, but with increase of Brix refractometer cut-off from 8.2 to 8.5%, SeRID and SeTIR were decreased from 96.0% (95% PCI: 88.0-99.0) and 79.0% (95% PCI: 70.0-85.0), to 92.0% (95% PCI: 77.0-99.0) and 74.0% (95% PCI: 61.0-82.0), respectively. SpRID and SpTIR were always higher than SpBrix at all tested cut-offs and were above 92.0%, and 96.0%, respectively. With increasing the cut-off of Brix refractometer from 8.2 to 8.5%, SeBrix estimate has remarkably increased from 79.0% (95% PCI: 70.0-96.0) to 95.0% (95% PCI: 87.0-100.0), respectively. Whilst, SpBrix was decreased from 95.0% (95% PCI: 91.0-98.0) at cut-off 8.2% to 84.0% (95% PCI: 78.0-94.0) at cut-off 8.5%. In conclusion, RID has a higher Se than TIR and Brix, if the latter is used with cut-offs of 8.2% or 8.3%. However, the higher the cut-off, the more comparable sensitivities of RID and digital Brix refractometer. The median estimate of SpTIR was always higher than SpRID and SpBrix at all tested cut-offs. However, the 95% confidence interval estimates of the three tests were overlapping across the tested cut-offs of digital Brix refractometer reflecting the inability to prefer a test over the other based on the Sp estimate.


Assuntos
Bovinos/imunologia , Imunidade Materno-Adquirida/fisiologia , Imunização Passiva/veterinária , Animais , Animais Recém-Nascidos , Proteínas Sanguíneas/análise , Feminino , Imunização Passiva/normas , Imunodifusão/veterinária , Análise de Classes Latentes , Gravidez , Refratometria/veterinária , Sensibilidade e Especificidade , Espectrofotometria Infravermelho/veterinária
8.
J Dairy Sci ; 102(1): 567-577, 2019 Jan.
Artigo em Inglês | MEDLINE | ID: mdl-30415862

RESUMO

The objectives of this study were (1) to determine the differences in IgG and total protein (TP) content of serum and plasma samples collected from the same calves; (2) to evaluate the correlation between calf serum and plasma IgG levels, Brix scores, and TP concentrations; (3) to determine whether different cut-off values should be used for plasma and serum to assess failure of transfer of passive immunity (FTPI) in dairy calves; and (4) to evaluate the level of agreement between results obtained from using serum and plasma samples of the same calves to assess FTPI using optimal cut-off values. Blood samples (n = 217) were collected from Holstein calves at 3 to 10 d of age on 30 commercial dairy farms in Nova Scotia and Newfoundland, Canada. Paired serum and plasma samples were analyzed for IgG concentration by the reference radial immunodiffusion assay, transmission infrared (TIR) spectroscopy, digital and optical Brix refractometers, and optical TP refractometer. The IgG concentrations measured by RID and TIR spectroscopy in serum were similar to those in plasma. However, the Brix and TP refractometer readings were significantly higher in plasma than in serum. The prevalence of FTPI in serum and plasma samples based on a RID-IgG concentration <10 g/L was 43.3 and 46.5%, respectively. The RID-IgG concentration was correlated with TIR-IgG (r = 0.92 and 0.89), digital Brix (r = 0.80 and 0.80), optical Brix (r = 0.77 and 0.77), and optical TP (r = 0.75 and 0.77) refractometers in serum and plasma, respectively. The correlations between paired serum and plasma IgG content were 0.85 by TIR spectroscopy, 0.80 by digital Brix, 0.77 by optical Brix, and 0.79 by optical TP refractometer. The optimal cut-off values for TIR spectroscopy, digital Brix, optical Brix, and TP refractometers to assess FTPI using serum were 13.1 g/L, 8.7% Brix, 8.4% Brix and 5.1 g/dL, respectively; and the optimal cut-off values with plasma were 13.4 g/L, 9.4% Brix, 9.3% Brix and 5.8 g/dL, respectively. When using these optimal cut-off values, the level of agreement (88.1%) between results derived from testing serum and plasma by TIR spectroscopy was substantial, with a kappa (κ) value of 0.76. The results derived from testing serum and plasma by digital Brix refractometer showed substantial agreement (83.4%), with a κ value of 0.65, which is higher than the agreement and κ value (74.7% and 0.51) reported for the optical Brix refractometer. Substantial agreement (81.6%) between serum and plasma TP was also obtained when using the optical TP refractometer, with a κ value of 0.63. In conclusion, serum or plasma samples can be used interchangeably for measuring IgG concentrations and assessing FTPI in dairy calves. However, different cut-offs must be used to assess FTPI depending on the sample matrix. Furthermore, results obtained from serum samples showed higher agreement with the reference RID assay than those obtained from plasma samples.


Assuntos
Proteínas Sanguíneas/análise , Bovinos/imunologia , Imunidade Materno-Adquirida/imunologia , Imunoglobulina G/sangue , Plasma/imunologia , Soro/imunologia , Animais , Animais Recém-Nascidos/imunologia , Canadá , Colostro/química , Feminino , Imunodifusão/veterinária , Refratometria/veterinária , Sensibilidade e Especificidade , Espectrofotometria Infravermelho/veterinária
9.
J Dairy Sci ; 101(11): 10048-10061, 2018 Nov.
Artigo em Inglês | MEDLINE | ID: mdl-30197141

RESUMO

Cheese-making properties of pressed cooked cheeses (PCC) and soft cheeses (SC) were predicted from mid-infrared (MIR) spectra. The traits that were best predicted by MIR spectra (as determined by comparison with reference measurements) were 3 measures of laboratory cheese yield, 5 coagulation traits, and 1 acidification trait for PCC (initial pH; pH0PPC). Coefficients of determination of these traits ranged between 0.54 and 0.89. These 9 traits as well as milk composition traits (fatty acid, protein, mineral, lactose, and citrate content) were then predicted from 1,100,238 MIR spectra from 126,873 primiparous Montbéliarde cows. Using this data set, we estimated the corresponding genetic parameters of these traits by REML procedures. A univariate or bivariate repeatability animal model was used that included the fixed effects of herd × test day × spectrometer, stage of lactation, and year × month of calving as well as the random additive genetic, permanent environmental, and residual effects. Heritability estimates varied between 0.37 and 0.48 for the 9 cheese-making property traits analyzed. Coagulation traits were the ones with the highest heritability (0.42 to 0.48), whereas cheese yields and pH0 PPC had the lowest heritability (0.37 to 0.39). Strong favorable genetic correlations, with absolute values between 0.64 and 0.97, were found between different measures of cheese yield, between coagulation traits, between cheese yields and coagulation traits, and between coagulation traits measured for PCC and SC. In contrast, the genetic correlations between milk pH0 PPC and CY or coagulation traits were weak (-0.08 to 0.09). The genetic relationships between cheese-making property traits and milk composition were moderate to high. In particular, high levels of proteins, fatty acids, Ca, P, and Mg in milk were associated with better cheese yields and improved coagulation. Proteins in milk were strongly genetically correlated with coagulation traits and, to a lesser extent, with cheese yields, whereas fatty acids in milk were more genetically correlated with cheese yields than with coagulation traits. This study, carried out on a large scale in Montbéliarde cows, shows that MIR predictions of cheese yields and milk coagulation properties are sufficiently accurate to be used for genetic analyses. Cheese-making traits, as predicted from MIR spectra, are moderately heritable and could be integrated into breeding objectives without additional phenotyping cost, thus creating an opportunity for efficient improvement via selection.


Assuntos
Cruzamento/métodos , Bovinos/genética , Queijo , Leite/química , Espectrofotometria Infravermelho/veterinária , Animais , Queijo/análise , Fenômenos Químicos , Ácidos Graxos/análise , Feminino , Manipulação de Alimentos/métodos , Lactose/análise , Proteínas do Leite/análise , Gravidez , Característica Quantitativa Herdável , Espectrofotometria Infravermelho/métodos
10.
Anim Sci J ; 89(11): 1622-1627, 2018 Nov.
Artigo em Inglês | MEDLINE | ID: mdl-30221430

RESUMO

This study estimated the effect of Holstein-Friesian, Brown Swiss, Jersey, Simmental and Alpine Grey cattle breeds on milk mineral contents (Ca, Mg, P, K, and Na) in multibreed herds using data predicted with mid-infrared spectroscopy. The dataset included 139,821 observations from 16,566 cows and 977 herds. Fixed effects considered in the mixed model were breed, parity, stage of lactation and first-order interactions, and random effects were cow, herd-test-date, and the residual. Multiple comparisons of least squares means were performed for the main effect of breed, parity, and stage of lactation using Bonferroni adjustment. Holstein-Friesian yielded milk with the lowest fat, protein, and casein concentration, and Ca, Mg, and P contents, whereas Jersey cows produced milk with the greatest fat, protein, and casein concentration, and Ca and Mg contents. Results of this study suggest that mixing milk from different breeds could enhance milk composition and technological ability, and therefore contribute to improve dairy industry efficiency.


Assuntos
Cruzamento , Indústria de Laticínios/métodos , Leite/química , Minerais/análise , Animais , Cálcio/análise , Caseínas/análise , Bovinos , Gorduras/análise , Feminino , Lactação/fisiologia , Magnésio/análise , Proteínas do Leite/análise , Paridade/fisiologia , Fósforo/análise , Espectrofotometria Infravermelho/veterinária
11.
J Dairy Sci ; 101(8): 7618-7624, 2018 Aug.
Artigo em Inglês | MEDLINE | ID: mdl-29753478

RESUMO

Evaluation and mitigation of enteric methane (CH4) emissions from ruminant livestock, in particular from dairy cows, have acquired global importance for sustainable, climate-smart cattle production. Based on CH4 reference measurements obtained with the SF6 tracer technique to determine ruminal CH4 production, a current equation permits evaluation of individual daily CH4 emissions of dairy cows based on milk Fourier transform mid-infrared (FT-MIR) spectra. However, the respiration chamber (RC) technique is considered to be more accurate than SF6 to measure CH4 production from cattle. This study aimed to develop an equation that allows estimating CH4 emissions of lactating cows recorded in an RC from corresponding milk FT-MIR spectra and to challenge its robustness and relevance through validation processes and its application on a milk spectral database. This would permit confirming the conclusions drawn with the existing equation based on SF6 reference measurements regarding the potential to estimate daily CH4 emissions of dairy cows from milk FT-MIR spectra. A total of 584 RC reference CH4 measurements (mean ± standard deviation of 400 ± 72 g of CH4/d) and corresponding standardized milk mid-infrared spectra were obtained from 148 individual lactating cows between 7 and 321 d in milk in 5 European countries (Germany, Switzerland, Denmark, France, and Northern Ireland). The developed equation based on RC measurements showed calibration and cross-validation coefficients of determination of 0.65 and 0.57, respectively, which is lower than those obtained earlier by the equation based on 532 SF6 measurements (0.74 and 0.70, respectively). This means that the RC-based model is unable to explain the variability observed in the corresponding reference data as well as the SF6-based model. The standard errors of calibration and cross-validation were lower for the RC model (43 and 47 g/d vs. 66 and 70 g/d for the SF6 version, respectively), indicating that the model based on RC data was closer to actual values. The root mean squared error (RMSE) of calibration of 42 g/d represents only 10% of the overall daily CH4 production, which is 23 g/d lower than the RMSE for the SF6-based equation. During the external validation step an RMSE of 62 g/d was observed. When the RC equation was applied to a standardized spectral database of milk recordings collected in the Walloon region of Belgium between January 2012 and December 2017 (1,515,137 spectra from 132,658 lactating cows in 1,176 different herds), an average ± standard deviation of 446 ± 51 g of CH4/d was estimated, which is consistent with the range of the values measured using both RC and SF6 techniques. This study confirmed that milk FT-MIR spectra could be used as a potential proxy to estimate daily CH4 emissions from dairy cows provided that the variability to predict is covered by the model.


Assuntos
Bovinos/metabolismo , Análise de Fourier , Metano/análise , Leite/química , Espectrofotometria Infravermelho/veterinária , Animais , Feminino , Lactação , Espectrofotometria Infravermelho/métodos
12.
J Dairy Sci ; 101(7): 6232-6243, 2018 Jul.
Artigo em Inglês | MEDLINE | ID: mdl-29605317

RESUMO

Mid-infrared (MIR) spectroscopy of milk was used to predict dry matter intake (DMI) and net energy intake (NEI) in 160 lactating Norwegian Red dairy cows. A total of 857 observations were used in leave-one-out cross-validation and external validation to develop and validate prediction equations using 5 different models. Predictions were performed using (multiple) linear regression, partial least squares (PLS) regression, or best linear unbiased prediction (BLUP) methods. Linear regression was implemented using just milk yield (MY) or fat, protein, and lactose concentration in milk (Mcont) or using MY together with body weight (BW) as predictors of intake. The PLS and BLUP methods were implemented using just the MIR spectral information or using the MIR together with Mcont, MY, BW, or NEI from concentrate (NEIconc). When using BLUP, the MIR spectral wavelengths were always treated as random effects, whereas Mcont, MY, BW, and NEIconc were considered to be fixed effects. Accuracy of prediction (R) was defined as the correlation between the predicted and observed feed intake test-day records. When using the linear regression method, the greatest R of predicting DMI (0.54) and NEI (0.60) in the external validation was achieved when the model included both MY and BW. When using PLS, the greatest R of predicting DMI (0.54) and NEI (0.65) in the external validation data set was achieved when using both BW and MY as predictors in combination with the MIR spectra. When using BLUP, the greatest R of predicting DMI (0.54) in the external validation was when using MY together with the MIR spectra. The greatest R of predicting NEI (0.65) in the external validation using BLUP was achieved when the model included both BW and MY in combination with the MIR spectra or when the model included both NEIconc and MY in combination with MIR spectra. However, although the linear regression coefficients of actual on predicted values for DMI and NEI were not different from unity when using PLS, they were less than unity for some of the models developed using BLUP. This study shows that MIR spectral data can be used to predict NEI as a measure of feed intake in Norwegian Red dairy cattle and that the accuracy is augmented if additional, often available data are also included in the prediction model.


Assuntos
Peso Corporal/imunologia , Bovinos , Ingestão de Energia/fisiologia , Leite/química , Espectrofotometria Infravermelho/veterinária , Animais , Bovinos/metabolismo , Feminino , Lactação , Valor Preditivo dos Testes , Espectrofotometria Infravermelho/métodos
13.
J Anim Sci ; 96(5): 1914-1928, 2018 May 04.
Artigo em Inglês | MEDLINE | ID: mdl-29518201

RESUMO

Six ruminally cannulated cows (570 ± 73 kg) fed corn residues were placed in a 6 × 6 Latin square to evaluate predictions of diet composition from ruminally collected diet samples. After complete ruminal evacuation, cows were fed 1-kg meals (dry matter [DM]-basis) containing different combinations of cornstalk and leaf and husk (LH) residues in ratios of 0:100, 20:80, 40:60, 60:40, 80:20, and 100:0. Diet samples from each meal were collected by removal of ruminal contents after 1-h and were either unrinsed, hand-rinsed or machine-rinsed to evaluate effects of endogenous compounds on predictions of diet composition. Diet samples were analyzed for neutral (NDF) and acid (ADF) detergent fiber, acid detergent insoluble ash (ADIA), acid detergent lignin (ADL), crude protein (CP), and near infrared reflectance spectroscopy (NIRS) to calculate diet composition. Rinsing type increased NDF and ADF content and decreased ADIA and CP content of diet samples (P < 0.01). Rinsing tended to increase (P < 0.06) ADL content of diet samples. Differences in concentration between cornstalk and LH residues within each chemical component were standardized by calculating a coefficient of variation (CV). Accuracy and precision of estimates of diet composition were analyzed by regressing predicted diet composition and known diet composition. Predictions of diet composition were improved by increasing differences in concentration of chemical components between cornstalk and LH residues up to a CV of 22.6 ± 5.4%. Predictions of diet composition from unrinsed ADIA and machine-rinsed NIRS had the greatest accuracy (slope = 0.98 and 0.95, respectively) and large coefficients of determination (r2 = 0.86 and 0.74, respectively). Subsequently, a field study (Exp. 2) was performed to evaluate predictions of diet composition in cattle (646 ± 89 kg) grazing corn residue. Five cows were placed in 1 of 10 paddocks and allowed to graze continuously or to strip-graze corn residues. Predictions of diet composition from ADIA, ADL, and NIRS did not differ (P = 0.99), and estimates of cornstalk intake tended to be greater (P = 0.09) in strip-grazed compared to continuously grazed cows. These data indicate that diet composition can be predicted by chemical components or NIRS by ruminal collection of diet samples among cattle grazing corn residues.


Assuntos
Bovinos/fisiologia , Dieta/veterinária , Fibras na Dieta , Animais , Digestão , Feminino , Lactação , Lignina/análise , Rúmen/metabolismo , Espectrofotometria Infravermelho/veterinária , Zea mays/química
14.
J Anim Sci ; 96(2): 771-782, 2018 Mar 06.
Artigo em Inglês | MEDLINE | ID: mdl-29385472

RESUMO

The objective of this study was to explore the potential of transmission infrared (TIR) spectroscopy in combination with partial least squares regression (PLSR) for quantification of dairy and beef cow colostral immunoglobulin G (IgG) concentration and assessment of colostrum quality. A total of 430 colostrum samples were collected from dairy (n = 235) and beef (n = 195) cows and tested by a radial immunodiffusion (RID) assay and TIR spectroscopy. Colostral IgG concentrations obtained by the RID assay were linked to the preprocessed spectra and divided into combined and prediction data sets. Three PLSR calibration models were built: one for the dairy cow colostrum only, the second for beef cow colostrum only, and the third for the merged dairy and beef cow colostrum. The predictive performance of each model was evaluated separately using the independent prediction data set. The Pearson correlation coefficients between IgG concentrations as determined by the TIR-based assay and the RID assay were 0.84 for dairy cow colostrum, 0.88 for beef cow colostrum, and 0.92 for the merged set of dairy and beef cow colostrum. The average of the differences between colostral IgG concentrations obtained by the RID- and TIR-based assays were -3.5, 2.7, and 1.4 g/L for dairy, beef, and merged colostrum samples, respectively. Further, the average relative error of the colostral IgG predicted by the TIR spectroscopy from the RID assay was 5% for dairy cow, 1.2% for beef cow, and 0.8% for the merged data set. The average intra-assay CV% of the IgG concentration predicted by the TIR-based method were 3.2%, 2.5%, and 6.9% for dairy cow, beef cow, and merged data set, respectively.The utility of TIR method for assessment of colostrum quality was evaluated using the entire data set and showed that TIR spectroscopy accurately identified the quality status of 91% of dairy cow colostrum, 95% of beef cow colostrum, and 89% and 93% of the merged dairy and beef cow colostrum samples, respectively. The results showed that TIR spectroscopy demonstrates potential as a simple, rapid, and cost-efficient method for use as an estimate of IgG concentration in dairy and beef cow colostrum samples and assessment of colostrum quality. The results also showed that merging the dairy and beef cow colostrum sample data sets improved the predictive ability of the TIR spectroscopy.


Assuntos
Bovinos , Colostro/química , Imunoglobulina G/química , Espectrofotometria Infravermelho/veterinária , Animais , Feminino , Imunodifusão , Análise dos Mínimos Quadrados , Gravidez , Espectrofotometria Infravermelho/métodos
15.
J Dairy Sci ; 101(3): 2260-2272, 2018 Mar.
Artigo em Inglês | MEDLINE | ID: mdl-29331465

RESUMO

Individual wavenumbers of the infrared (IR) spectra of bovine milk have been shown to be moderately to highly heritable. The objective of this study was to identify genomic regions associated with individual milk IR wavenumbers. This is expected to provide information about the genetic background of milk composition and give insight in the relation between IR wavenumbers and milk components. For this purpose, a genome-wide association study was performed for a selected set of 50 individual IR wavenumbers measured on 1,748 Dutch Holstein cows. Significant associations were detected for 28 of the 50 wavenumbers. In total, 24 genomic regions distributed over 16 bovine chromosomes were identified. Major genomic regions associated with milk IR wavenumbers were identified on chromosomes 1, 5, 6, 14, 19, and 20. Most of these regions also showed significant associations with fat, protein, or lactose percentage. However, we also identified some new regions that were not associated with any one of these routinely collected milk composition traits. On chromosome 1, we identified 2 new genomic regions and hypothesized that they are related to variation in milk phosphorus content and orotic acid, respectively. On chromosome 20, we identified a new genomic region that seems to be related to citric acid. Identification of genomic regions associated with milk phosphorus content, orotic acid, and citric acid suggest that the milk IR spectra contain direct information on these milk components. Consequently milk IR analyses probably can be used to predict these milk components, which have low concentrations in milk; this can lead to novel applications of milk IR spectroscopy for dairy cattle breeding and herd management.


Assuntos
Bovinos/genética , Estudo de Associação Genômica Ampla , Leite/química , Espectrofotometria Infravermelho/veterinária , Animais , Cruzamento , Gorduras/análise , Feminino , Lactose/análise , Proteínas do Leite/análise , Fenótipo , Característica Quantitativa Herdável
16.
J Vet Intern Med ; 32(1): 491-496, 2018 Jan.
Artigo em Inglês | MEDLINE | ID: mdl-29280196

RESUMO

BACKGROUND: Heat-treatment of colostrum is a method developed to reduce calf exposure to pathogens. Infrared (IR) spectroscopy and Brix refractometers can be used for measuring colostral IgG concentration and assessing colostrum quality. OBJECTIVES: To determine the impact of heat-treatment on accuracy of IR spectroscopy and Brix refractometers for measuring colostral IgG concentration and assessing colostrum quality before and after heat-treatment. ANIMALS: A total of 60 Holstein dairy cows on 8 commercial dairy farms. METHODS: A cross-sectional study was designed to determine the effect of heat-treatment at 60°C and 63°C each for 30 and 60 minutes duration on colostral IgG concentration measured by the reference radial immunodiffusion (RID) assay, IR spectroscopy, and digital and optical refractometers. RESULTS: Colostrum IgG concentration significantly decreased after heat-treatment at 63°C for 30 or 60 minutes as measured by RID, but the IgG values remained unchanged when measured by IR spectroscopy and refractometers. The lowest correlation coefficient found between IR spectroscopy (r = 0.70) and RID results was in colostrum heat-treated at 63°C for 60 minutes. For digital (r = 0.48) and optical (r = 0.50) refractometers, the lowest correlation coefficient was at 63°C for 30 minutes when compared to RID. The accuracy of the IR spectroscopy, digital and optical Brix refractometers was decreased from 91.7 to 80%, 81.7 to 45%, and 80 to 45%, respectively, when colostrum heat-treated at 63°C for 60 minutes. CONCLUSIONS AND CLINICAL IMPORTANCE: Radial immunodiffusion, IR spectroscopy, and Brix refractometers exhibit utility for measuring IgG concentration when colostrum heat-treated at 60°C but does not detect decrease IgG concentrations when heat-treated at 63°C.


Assuntos
Colostro/química , Imunoglobulina G/análise , Refratometria/veterinária , Espectrofotometria Infravermelho/veterinária , Animais , Bovinos , Estudos Transversais , Feminino , Temperatura Alta , Pasteurização , Refratometria/métodos , Espectrofotometria Infravermelho/métodos
17.
J Dairy Sci ; 100(11): 8705-8721, 2017 Nov.
Artigo em Inglês | MEDLINE | ID: mdl-28865855

RESUMO

The aims of this work were (1) to develop prediction equations from mid-infrared spectroscopy (MIRS) to establish a detailed fatty acid (FA) composition of milk; (2) to propose a milk FA index, utilizing MIRS-developed equations, in which the precision of the FA-prediction equations is taken into account to increase the value of milk; and (3) to show application examples. A total of 651 bulk cow milk samples were collected from 245 commercial farms in northwest Italy. The results of the 651 gas chromatography analyses were used to establish (421 samples) and to validate (230 samples) the outcomes of the FA composition prediction that had been obtained by MIRS. A class-based approach, in which the obtained MIRS equations were used, was proposed to define a milk classification. The method provides a numerical index [milk FA index (MFAI)] that allows a premium price to be quantified to increase the value of a favorable FA profile of milk. Ten FA were selected to calculate MFAI, according to their relevance for human health and potential cheese sensory properties, and animal welfare and environmental sustainability were also considered. These factors were selected as dimensions of MFAI. A statistical analysis and expert judgment aggregation were performed on the selected FA by weighting the FA and normalizing the dimensions to reduce redundancy. A class approach was applied, using the precision of the MIRS equations to establish the classes. The median FA concentration of the data set was set as a reference value of class 0. The width, number, and limits of classes above and below the median were calculated using the 95% confidence level of the standard error of prediction, corrected with the bias of each FA. A progressive number and a positive or negative sign were assigned to each FA class above or below the median according to their role in the above mentioned dimensions. The sum of the numbers of each class, associated with its sign for each FA, was used to generate MFAI. The MFAI was applied to dairy farms characterized by different feeding strategies, all of which deliver milk to a commercial dairy plant. The MFAI values ranged from 0.7 to 4.2, and large variations, which depended on the cows' diet and forage quality, were observed for each feeding system. The proposed method has been found to be flexible and adaptable to several contexts on both intensive and extensive dairy farms.


Assuntos
Ácidos Graxos/análise , Leite/química , Espectrofotometria Infravermelho/veterinária , Animais , Bovinos , Cromatografia Gasosa/veterinária , Indústria de Laticínios/métodos , Dieta/veterinária , Feminino , Itália
18.
J Dairy Sci ; 100(7): 5578-5591, 2017 Jul.
Artigo em Inglês | MEDLINE | ID: mdl-28527796

RESUMO

Many countries have pledged to reduce greenhouse gases. In this context, the dairy sector is one of the identified sectors to adapt production circumstances to address socio-environmental constraints due to its large carbon footprint related to CH4 emission. This study aimed mainly to estimate (1) the genetic parameters of 2 milk mid-infrared-based CH4 proxies [predicted daily CH4 emission (PME, g/d), and log-transformed predicted CH4 intensity (LMI)] and (2) their genetic correlations with milk production traits [milk (MY), fat (FY), and protein (PY) yields] from first- and second-parity Holstein cows. A total of 336,126 and 231,400 mid-infrared CH4 phenotypes were collected from 56,957 and 34,992 first- and second-parity cows, respectively. The PME increased from the first to the second lactation (433 vs. 453 g/d) and the LMI decreased (2.93 vs. 2.86). We used 20 bivariate random regression test-day models to estimate the variance components. Moderate heritability values were observed for both CH4 traits, and those values decreased slightly from the first to the second lactation (0.25 ± 0.01 and 0.22 ± 0.01 for PME; 0.18 ± 0.01 and 0.17 ± 0.02 for LMI). Lactation phenotypic and genetic correlations were negative between PME and MY in both first and second lactations (-0.07 vs. -0.07 and -0.19 vs. -0.24, respectively). More close scrutiny revealed that relative increase of PME was lower with high MY levels even reverting to decrease, and therefore explaining the negative correlations, indicating that higher producing cows could be a mitigation option for CH4 emission. The PME phenotypic correlations were almost equal to 0 with FY and PY for both lactations. However, the genetic correlations between PME and FY were slightly positive (0.11 and 0.12), whereas with PY the correlations were slightly negative (-0.05 and -0.04). Both phenotypic and genetic correlations between LMI and MY or PY or FY were always relatively highly negative (from -0.21 to -0.88). As the genetic correlations between PME and LMI were strong (0.71 and 0.72 in first and second lactation), the selection of one trait would also strongly influence the other trait. However, in animal breeding context, PME, as a direct quantity CH4 proxy, would be preferred to LMI, which is a ratio trait of PME with a trait already in the index. The range of PME sire estimated breeding values were 22.1 and 29.41 kg per lactation in first and second parity, respectively. Further studies must be conducted to evaluate the effect of the introduction of PME in a selection index on the other traits already included in this index, such as, for instance, fertility or longevity.


Assuntos
Lactação/genética , Metano/metabolismo , Leite/metabolismo , Animais , Cruzamento , Bovinos , Feminino , Modelos Lineares , Metano/análise , Paridade , Fenótipo , Gravidez , Espectrofotometria Infravermelho/veterinária
19.
J Dairy Sci ; 100(6): 5073-5081, 2017 Jun.
Artigo em Inglês | MEDLINE | ID: mdl-28434722

RESUMO

The fatty acid profile of milk is a prevailing issue due to the potential negative or positive effects of different fatty acids to human health and nutrition. Mid-infrared spectroscopy can be used to obtain predictions of otherwise costly fatty acid phenotypes in a widespread and rapid manner. The objective of this study was to evaluate the prediction of fatty acid content for the Canadian dairy cattle population from mid-infrared spectral data and to compare the results produced by altering the partial least squares (PLS) model development set used. The PLS model development sets used to develop the predictions were reference fatty acids expressed as (1) grams per 100 g of fatty acid, (2) grams per 100 g of milk, (3) the natural logarithmic transform of grams per 100 g of milk, and (4) subsets of samples randomly selected by removing excess records around the mean to present a more uniform distribution, repeated 10 times. Gas chromatography measured fatty acid concentration and spectral data for 2,023 milk samples of 373 cows from 4 breeds and 44 herds were used in the model development. The coefficient of determination of cross-validation (Rcv2) increased when fatty acids were expressed on a per 100 g of milk basis compared with on a per 100 g of fat basis for all examined fatty acids. The logarithmic transformation used to create a more Gaussian distribution in the development set had little effect on the prediction accuracy. The individual fatty acids C12:0, C14:0, C16:0, C18:0, C18:1n-9 cis, and saturated, monounsaturated, unsaturated, short-chain, medium-chain, and long-chain fatty acid groups had (Rcv2) greater than 0.70. When model development was performed with subsets of the original samples, slight increases in (Rcv2) values were observed for the majority of fatty acids. The difference in (Rcv2) between the top- and bottom-performing prediction equation across the different subsets for a single predicted fatty acid was on average 0.055 depending on which samples were randomly selected to be used in the PLS model development set. Predictions for fatty acids with high accuracies can be used to monitor fatty acid contents for cows in milk recording programs and possibly for genetic evaluation.


Assuntos
Ácidos Graxos/análise , Leite/química , Animais , Canadá , Bovinos , Feminino , Distribuição Normal , Espectrofotometria Infravermelho/veterinária
20.
J Dairy Sci ; 100(3): 1640-1649, 2017 Mar.
Artigo em Inglês | MEDLINE | ID: mdl-28088404

RESUMO

The objectives of this study were to investigate the sources of variation in milk fat globule (MFG) size in bovine milk and its prediction using mid-infrared (MIR) spectroscopy. Mean MFG size was measured in 2,076 milk samples from 399 Ayrshire, Brown Swiss, Holstein, and Jersey cows, and expressed as volume moment mean (D[4,3]) and surface moment mean (D[3,2]). The mid-infrared spectra of the samples and milk performance data were also recorded during routine milk recording and testing. The effects of breed, herd nested within breed, days in milk, season, milking period, age at calving, parity, and individual animal on the variation observed in MFG size were investigated. Breed, herd nested within breed, days in milk, season, and milking period significantly affected mean MFG size. Milk fat globule size was the largest at the beginning of lactation and subsequently decreased. Milk samples with the smallest MFG on average came from Holstein cows, and those with the largest were from Jersey and Brown Swiss cows. Partial least squares regression was used to predict MFG size from MIR spectra of samples with a calibration data set containing 2,034 and 2,032 samples for D[4,3] and D[3,2], respectively. Coefficients of determination of cross validation for D[4,3] and D[3,2] prediction models were 0.51 and 0.54, respectively. The associated ratio of performance deviation values were 1.43 and 1.48 for D[4,3] and D[3,2], respectively. With these models, individual mean MFG size could not be accurately predicted, but results may be sufficient to screen samples for having either small or large MFG on average. Significant but low correlations of D[4,3] and D[3,2] with milk fat yield were estimated (0.16 and 0.21, respectively). Significant and moderate Pearson correlation coefficients for fat percent with D[4,3] and D[3,2] were assessed (0.34 and 0.36, respectively). This correlation was greater between milk fat percentage and predicted MFG size than with measured MFG size with coefficients of 0.47 and 0.49 for D[4,3] and D[3,2], respectively. The MIR prediction equations are potentially overusing the correlation between fat and MFG size and exploiting the strong relationship between the MIR spectra and total milk fat. However, the predictions of MFG size are able to determine variation in mean globule size beyond what would be achieved just by looking at the correlation with fat production.


Assuntos
Lactação , Leite/química , Animais , Cruzamento , Bovinos , Feminino , Análise dos Mínimos Quadrados , Espectrofotometria Infravermelho/veterinária
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