Relationships between milk coagulation property traits analyzed with different methodologies

Abstract

Milk coagulation properties (MCP) analysis is performed using a wide range of methodologies in different countries and laboratories, using different instruments, coagulant activity in the milk, and type of coagulant. This makes it difficult to compare results and data from different research. The aims of this study were to propose a method for the transformation of values of rennet coagulation time (RCT) and curd firmness (a(30)) and to predict the noncoagulation (NC) probability of milk samples analyzed using different methodologies. Individual milk samples were collected during the morning milking in October 2010 from each of 165 Holstein-Friesian dairy cows in 2 freestall barns in Italy, and sent to 3 laboratories for MCP analysis. For each laboratory, MCP analysis was performed using a different methodology: A, with a computerized renneting meter instrument using 0.051 international milk clotting units (IMCU)/mL of coagulant activity; B, with a Lattodinamografo (Foss-Italia, Padova, Italy) using 0.051 IMCU/mL of coagulant activity; and C, with an Optigraph (Ysebaert, Frépillon, France) using 0.120 IMCU/mL of coagulant activity. The relationships between MCP traits were analyzed with correlation and regression analyses for each pair of methodologies. For each MCP trait, 2 regression models were applied: model 1 was a single regression model, where the dependent and independent variables were the same MCP trait determined by 2 different methodologies; in model 2, both a(30) and RCT were included as independent variables. The NC probabilities for laboratories with the highest number of NC samples were predicted based on the RCT and a(30) values measured in the laboratories with lower number of NC samples using logistic regression and receiver operating characteristic analysis. The percentages of NC samples were 4.2, 11.5, and 0.6% for A, B, and C, respectively. The transformation of MCP traits was more precise with model 1 for RCT (R(2): 0.77-0.82) than for a(30) (R(2): 0.28-0.63). The application of model 2 was needed when the C measurements were transformed into the other scales. The analyses of NC probabilities of milk samples showed that NC samples from one methodology were well distinguishable (with an accuracy of 0.972-0.996) based on the rennet coagulation time measured with the other methodology. A standard definition for MCP traits analysis is needed to enable reliable comparisons between MCP traits recorded in different laboratories and in different animal populations and breeds.

Genetic parameters for milk mineral content and acidity predicted by mid-infrared spectroscopy in Holstein–Friesian cows

“…Descriptive statistics of milk chemical composition and MY are in agreement with those documented in several studies on Italian Holstein − Friesian cows Pretto et al, 2011;Tiezzi et al, 2013), with the exception of SCS, which exhibited a smaller phenotypic coefficient of variation in the present study. Parity mean milk composition and yield (Table 2) were generally in agreement with other studied international populations of dairy cows (Ikonen et al, 2004;Hansen et al, 2006;Bastin et al, 2013).…”

Heritability and repeatability of milk coagulation properties predicted by mid-infrared spectroscopy during routine data recording, and their relationships with milk yield and quality traits

“…Vallas et al (2010) analyzed log-transformed RCT in Estonian Holstein population and found an average value of 2.3 (CV of 9%) and an average a 30 of 27.0 mm (CV of 27%). However, direct comparison of MCP among breeds reared in different countries has to be made with caution because dairy populations have different genetic structure, and laboratory analyses are known to differ in terms of instruments used and activity of the coagulant (Pretto et al, 2011).…”

Milk Protein Genotype Associations with Milk Coagulation and Quality Traits

“…The genetic correlations of −0.24 and −0.07 reported (Ikonen et al, 1999a(Ikonen et al, , 2004 for the same traits, however, are different. These inconsistencies indicate that different methodologies used for the investigations may influence the results (Pretto et al, 2011). Curd firmness showed a weak positive genetic correlation (Vallas et al, 2010) with milk fat percentage (0.25) and a weak negative genetic correlation with milk yield (−0.29).…”

“…Accordingly, breeding strategies for the enhancement of MCP based on MIR predictions as indicator traits could be easily and immediately implemented for dairy cattle populations where the routine acquisition of spectra from individual milk samples is already measured ). Nevertheless, MCP traits analyzed with different methodologies have significantly different values, due to the diversity of the instruments used and the coagulant activity (Pretto et al, 2011). The type of coagulant could also have an effect, since different coagulants have been used.…”

“…The type of coagulant could also have an effect, since different coagulants have been used. The method proposed for the prediction of non-coagulation probability of milk samples showed that non coagulating samples from one methodology were highly predictable based on the rennet coagulation time measured with another methodology (Pretto et al, 2011). A standard definition of MCP traits analysis is needed to enable reliable comparisons between MCP traits recorded in different laboratories, and in different animal populations and breeds.…”