Predictive ability of mid-infrared spectroscopy for major mineral composition and coagulation traits of bovine milk by using the uninformative variable selection algorithm

Abstract

Milk minerals and coagulation properties are important for both consumers and processors, and they can aid in increasing milk added value. However, large-scale monitoring of these traits is hampered by expensive and time-consuming reference analyses. The objective of the present study was to develop prediction models for major mineral contents (Ca, K, Mg, Na, and P) and milk coagulation properties (MCP: rennet coagulation time, curd-firming time, and curd firmness) using mid-infrared spectroscopy. Individual milk samples (n=923) of Holstein-Friesian, Brown Swiss, Alpine Grey, and Simmental cows were collected from single-breed herds between January and December 2014. Reference analysis for the determination of both mineral contents and MCP was undertaken with standardized methods. For each milk sample, the mid-infrared spectrum in the range from 900 to 5,000cm(-1) was stored. Prediction models were calibrated using partial least squares regression coupled with a wavenumber selection technique called uninformative variable elimination, to improve model accuracy, and validated both internally and externally. The average reduction of wavenumbers used in partial least squares regression was 80%, which was accompanied by an average increment of 20% of the explained variance in external validation. The proportion of explained variance in external validation was about 70% for P, K, Ca, and Mg, and it was lower (40%) for Na. Milk coagulation properties prediction models explained between 54% (rennet coagulation time) and 56% (curd-firming time) of the total variance in external validation. The ratio of standard deviation of each trait to the respective root mean square error of prediction, which is an indicator of the predictive ability of an equation, suggested that the developed models might be effective for screening and collection of milk minerals and coagulation properties at the population level. Although prediction equations were not accurate enough to be proposed for analytic purposes, mid-infrared spectroscopy predictions could be evaluated as phenotypic information to genetically improve milk minerals and MCP on a large scale.

Phenotypic analysis of milk coagulation properties and mineral content of Pinzgauer cattle breed

“…The greatest a 30 was observed at the onset of the lactation, it reached the lowest values between 1 and 7 months after calving, and increased thereafter. The better MCPs in early lactation agreed with the trend described by several authors (Ikonen et al, 2004;De Marchi et al, 2007;Penasa et al, 2014;Varotto et al, 2015;Visentin et al, 2015). Concentration of Ca and Mg decreased and then increased until 10 months from calving, while Na increased during lactation until 9 months from calving.…”

“…Among MCPs, a 30 was the trait with the greatest CV, in agreement with results from other authors (Toffanin et al, 2015;Varotto et al, 2015;Niero et al, 2016). As expected, milk was richer in K and Ca, and poorer in Mg (Gaucheron, 2005;Soyeurt et al, 2009;Niero et al, 2016;Visentin et al, 2016Visentin et al, , 2018. However, among minerals, K concentration showed the lowest CV (8.61 %) and Mg the greatest one (17.59 %; Table 1).…”

“…Moreover, mid-infrared spectra of the analysed samples were stored to allow the a posteriori prediction of MCPs and mineral contents using prediction models developed by Visentin et al (2016). Briefly, in 2014 a reference dataset containing information on 923 individual cow milk samples from several cattle breeds was collected in Bolzano province.…”

“…Concentrations of Ca, P and Mg in milk are negatively correlated with rennet coagulation time (RCT, min) and curd-firming time (k 20 , min), and positively correlated with curd firmness 30 min after rennet addition (a 30 , mm) and titratable acidity (Toffanin et al, 2015;Visentin et al, 2018). Prediction models for major milk minerals have been developed using spectra data recorded through mid-infrared spectroscopy (Soyeurt et al, 2009;Toffanin et al, 2015;Visentin et al, 2016). This has facilitated the phenotypic characterization of cattle breeds , otherwise impossible for the limitations of the standard methods (laborious, expensive and timeconsuming).…”

Are cheese-making properties of dual purpose cattle impaired by highland grazing? A case study using Aosta Red Pied cows

“…Milk Ca content was not measured in the present study. Overall, it is established that MCP are influenced also by other effects that have not been taken into account, such as laboratory procedures, detailed milk protein and mineral composition (Niero et al 2016a;Visentin et al 2016) and genetics of cows (Ikonen et al 2004). Nevertheless, since the cows used in the present study were the same on LO and HI, the latter factor was not contributing in the present study.…”