Genetic Background of Micellar and Soluble Calcium and Phosphorus Predicted From Bovine Milk Mid-infrared Spectra 

2020 ◽  
Author(s):  
Marco Franzoi ◽  
Angela Costa ◽  
Mauro Penasa ◽  
Massimo De Marchi
Keyword(s):  
Genetic Variation ◽  
Protein Content ◽  
Milk Yield ◽  
Infrared Spectra ◽  
Selection Index ◽  
Bovine Milk ◽  
Cow Milk ◽  
Future Research ◽  
Mid Infrared ◽  
Milk Coagulation

Abstract BackgroundMid-infrared spectroscopy (MIRS) is a valuable tool to determine milk composition and quality, and to collect data at population level. In milk, Ca and P are partitioned between micellar (MP) and soluble phase (SP), both with important effects on milk coagulation properties; in particular, greater mineral content in MP translates into better milk coagulation ability. Nevertheless, the high analytic costs of gold standard quantification methods hamper the possibility to deepen partition of minerals in MP and SP on a large scale. In this study, MP and SP of Ca and P were predicted from mid-infrared spectra of 111,653 individual milk samples from 9,519 Italian Holstein cows. Sources of non-genetic variation for MP and SP of Ca and P were investigated, and their genetic associations with milk yield, quality and coagulation properties were estimated.ResultsThe MP of Ca and P decreased with parity but increased along the lactation, resembling the trend of protein content. Both MP and SP of Ca and P showed exploitable genetic variation and were heritable, and they were associated with traits of interest for the dairy industry, in particular milk yield and protein content. Furthermore, negative correlations between the two phases of the same mineral were estimated. The MP was negatively related to milk yield.ConclusionsThe MP and SP of the same mineral are negatively correlated, meaning that it is possible to improve mineral partition toward MP, in order to get milk with better technological properties. The current selection index of Italian Holstein breed gives positive emphasis on milk protein (content and yield) and thus it is indirectly improving the MP of Ca and P while reducing their SP content. Future research will focus on the genomic architecture of such traits to evaluate the role of potential specific genes in the determination of these mineral fractions in cow milk.

Determination of Protein in Ground Wheat Samples by Mid-Infrared Diffuse Reflectance Spectroscopy

1997 ◽  
Vol 51 (8) ◽  
pp. 1200-1204 ◽  
Author(s):  
James B. Reeves ◽  
Stephen R. Delwiche
Keyword(s):  
Protein Content ◽  
Infrared Spectra ◽  
Diffuse Reflectance ◽  
Near Infrared ◽  
Diffuse Reflectance Spectroscopy ◽  
Reflectance Spectroscopy ◽  
Mid Infrared ◽  
Near Infrared Spectra ◽  
Hard Red Winter Wheat ◽  
Ground Wheat

The objective of this study was to determine whether mid-infrared diffuse reflectance spectroscopy could be used in the same manner as near-infrared diffuse reflectance spectroscopy to quantitatively determine the protein content of ground wheat samples. One hundred and thirty hard red winter wheat samples were assayed for protein by combustion and scanned in the near- and mid-infrared. Samples (UDY ground) were scanned neat in the near-infrared from 1100 nm (9091 cm−1) to 2498 nm (4003 cm−1) on a scanning monochromator and in the mid-infrared from 4000 cm−1 (2500 nm) to 400 cm−1 (25,000 nm) on a Fourier transform spectrometer at 4-and 16-cm−1 resolutions. Protein content varied from a low of 8.98% to a high of 18.70% (average of 12.86% with a standard deviation of 1.66%). Calibrations developed with the use of partial least-squares gave an R2 and bias-corrected standard error of performance of 0.999 and 0.054 for the near-infrared and 0.997 and 0.085 for the mid-infrared (4 cm−1 resolution). Calibration results based on mid-infrared spectra, while not as good as those for near-infrared spectra, were nevertheless quite good. These results demonstrate that it is possible to develop satisfactory calibrations for protein in ground wheat with the use of mid-infrared spectra without the need for sample dilution with KBr.

scholarly journals Prediction of bovine milk true protein content by mid-infrared spectroscopy

2011 ◽  
Vol 41 (8) ◽  
pp. 1472-1474 ◽  
Author(s):  
Bruno Garcia Botaro ◽  
Cristina Simões Cortinhas ◽  
Lucinéia Mestieri ◽  
Paulo Fernando Machado ◽  
Marcos Veiga dos Santos
Keyword(s):  
Protein Content ◽  
Crude Protein ◽  
Bovine Milk ◽  
Dairy Herds ◽  
Protein Determination ◽  
Mid Infrared ◽  
Kjeldahl Method ◽  
Bulk Tank ◽  
One Year ◽  
True Protein

The aim of this study was to estimate the concentration of milk true protein (TP) by mid-infrared absorbance method (MIR) in samples from bulk tank of dairy herds, and to determine the correlation between the results of TP of milk determined by Kjeldahl and MIR. Forty nine dairy herds were selected (17 Holstein, 6 Jersey and 26 Girolando) for monthly collections of samples from bulk tanks during the period of one year (284 samples). Fat, lactose, crude protein and total solids were firstly determined by MIR, and then analyzed for total and true protein by Kjeldahl method. The regression equation to estimate TP contents based on MIR crude protein determination was as follows: TP=0.0021+(1.0104xCP), where: TP is the content of true protein, CP is the crude protein content determined by the MIR method, and 0.0155 is the model error term.

scholarly journals Multivariate regression analysis of dairy characteristics of Holstein cows

2021 ◽  
Vol 214 (11) ◽  
pp. 27-35
Author(s):  
Galina Karlikova ◽  
A. Konte
Keyword(s):  
Protein Content ◽  
Milk Yield ◽  
Mass Fraction ◽  
High Reliability ◽  
Negative Relationship ◽  
Cow Milk ◽  
Holstein Cows ◽  
Coefficient Of Determination ◽  
Milk Productivity ◽  
Factor Regression

Abstract. The purpose of the research is the possibility of applying the equation of multiple two-factor regression to reveal the reliable conjugacy of signs affecting the level of milk productivity of Holstein cows. Research methods. The research was carried out in CJSC BP “Aksin’ino” of Stupinskiy district of the Moscow region. Based on the IAS “SELEX”, a database was created, including a sample of 11 017 heads. Data on milk productivity were taken for 305 days of 1st, 2nd, 3rd and maximum lactation. Results. For 305 days of the 1st lactation, milk yield averaged 7909.5 kg of milk, for the 2nd – 8289.1 kg (p ≤ 0,001) and the 3rd lactation – 8446.2 kg (p ≤ 0,001). Milk yield for maximum lactation was 8964.3 kg of milk (p ≤ 0.001). The fat and protein content in cow milk between the 1st and 3rd lactation is 4.03–4.08 and 3.22–3.23 %. The multiple two-factor regression coefficient represents the response bias from 7787.81–8239.00 (1st, 2nd, 3rd lactation) to 8841.63 (maximum lactation). The scattering diagrams of the multiple regression model show that the value of the variable "milk yield" is statistically dependent on the indicators of the mass fraction of fat and the mass fraction of protein in milk. The coefficient of determination at the level of 0.997–0.998 indicates that the regression equation explains 99.7–99.8 % of the variance of the effective feature. The significance of Fischer’s F-test indicates the high reliability of the results and the absence of randomness and the presence of a pattern justified in our study. The fat and protein content had a negative relationship with milk yield (p ≤ 0,001) by lactation. High values of the relationship between milk yield and protein content (–0.518…–0.766), fat and protein content (0.626–0,784) were obtained. Scientific novelty. For the first time, studies were conducted on the correspondence of the equation model to the experimental data obtained, and the presence of the number of independent variables (mass fraction of fat and protein) included in the equation to describe the dependent variable (milk yield).

scholarly journals A comparison of 4 different machine learning algorithms to predict lactoferrin content in bovine milk from mid-infrared spectra

2020 ◽  
Vol 103 (12) ◽  
pp. 11585-11596
Author(s):  
H. Soyeurt ◽  
C. Grelet ◽  
S. McParland ◽  
M. Calmels ◽  
M. Coffey ◽  
...  
Keyword(s):  
Machine Learning ◽  
Infrared Spectra ◽  
Learning Algorithms ◽  
Bovine Milk ◽  
Machine Learning Algorithms ◽  
Mid Infrared

scholarly journals Can We Observe Expected Behaviors at Large and Individual Scales for Feed Efficiency-Related Traits Predicted Partly from Milk Mid-Infrared Spectra?

Animals ◽  
2020 ◽  
Vol 10 (5) ◽  
pp. 873
Author(s):  
Lei Zhang ◽  
Nicolas Gengler ◽  
Frédéric Dehareng ◽  
Frédéric Colinet ◽  
Eric Froidmont ◽  
...  
Keyword(s):  
Milk Yield ◽  
Infrared Spectra ◽  
Feed Efficiency ◽  
Dry Matter ◽  
National Research Council ◽  
The Body ◽  
Dry Matter Intake ◽  
Mid Infrared ◽  
Stage Of Lactation ◽  
Consumption Index

Phenotypes related to feed efficiency were predicted from records easily acquired by breeding organizations. A total of 461,036 and 354,148 records were collected from the first and second parity Holstein cows. Equations were applied to the milk mid-infrared spectra to predict the main milk components and coupled with animal characteristics to predict the body weight (pBW). Dry matter intake (pDMI) was predicted from pBW using the National Research Council (NRC) equation. The consumption index (pIC) was estimated from pDMI and fat, and protein corrected milk. All traits were modeled using single trait test-day models. Descriptive statistics were within the expected range. Milk yield, pDMI, and pBW were phenotypically positively related (r ranged from 0.08 to 0.64). As expected, pIC was phenotypically negatively correlated with milk yield (−0.77 and −0.80 for the first and second lactation) and slightly positively correlated with pBW (0.16 and 0.07 for the first and second lactation). Later, parity cows seemed to have a better feed efficiency as they had a lower pIC. Although the prediction accuracy was moderate, the observed behaviors of studied traits by year, stage of lactation, and parity were in agreement with the literature. Moreover, as a genetic component was highlighted (heritability around 0.18), it would be interesting to realize a genetic evaluation of these traits and compare the obtained breeding values with the ones estimated for sires having daughters with reference feed efficiency records.

Limitations and potential for individualised feeding of concentrate supplements to grazing dairy cows

2015 ◽  
Vol 55 (7) ◽  
pp. 922 ◽  
Author(s):  
J. L. Hills ◽  
S. C. García ◽  
B. Dela Rue ◽  
C. E. F. Clark
Keyword(s):  
Milk Yield ◽  
Cow Milk ◽  
Future Research ◽  
Feeding Strategies ◽  
Important Distinction ◽  
Large Variability ◽  
Flat Rate ◽  
Stage Of Lactation ◽  
Ad Libitum ◽  
Grazing Dairy Cows

The increasing availability and installation of computerised feeding and milk-monitoring technology in Australia and New Zealand has led to an increased interest in feeding individual cows different amounts and types of supplements over lactation. However, there is confusion about the potential benefits of individualised feeding strategies compared with feeding the same amount of supplement to all cows in the herd on any given day. The majority of bail feeding research conducted over the past 30 years has identified little difference in cow response between flat-rate feeding and more complicated approaches of split feeding or feeding to individual cow milk yield. However, it must be noted that many of these experiments involved animals with ad libitum access to a forage supply. This is an important distinction as individual cows receiving high-quality forage ad libitum would be able to compensate, at least partially, for any shortage in concentrate. Large variability in response to supplements among individual cows within the herd implies that there should be a benefit from individualised bail feeding practices. This review examines the potential for individualised bail feeding in pasture-based dairy systems, considering both system (pasture allowance and type) and cow-level parameters (e.g. dry matter intake, milk yield, genotype, bodyweight, stage of lactation) that could affect the individual cow response to a particular supplement, and discusses the current limitations and future challenges for implementing this technology on farm. Recommendations for future research are made to address any knowledge gaps.

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