Reproducibility and Repeatability of Measures of Milk Coagulation Properties and Predictive Ability of Mid-Infrared Reflectance Spectroscopy

Abstract

The objectives of the study were to estimate the reproducibility and repeatability of milk coagulation properties (MCP) measured by a computerized renneting meter (CRM) and to evaluate the predictive ability of mid-infrared spectroscopy (MIRS) as an innovative technology for the assessment of rennet coagulation time (RCT, min) and curd firmness (a(30), mm). Four samples without addition of preservative (NP) and 4 samples with Bronopol addition (PS) were collected from each of 83 Holstein-Friesian cows. Six hours after collection, 2 replicated measures of MCP were obtained with CRM using 1 NP and 1 PS sample from each cow. Mid-infrared spectra of the remaining NP and PS samples from each animal were recorded after 6 h, 4 d, and 8 d after sampling. Two groups of calibration equations were developed using MIRS spectra and CRM measures of MCP as reference data obtained from analysis of NP and PS, respectively. Reproducibility and repeatability of CRM measures were obtained from REML estimation of variance components on the basis of a linear model including the fixed effects of herd and days in milk class and the random effects of cows, sample treatment (addition or no addition of preservative), and the interaction between cow and sample treatment. Coefficient of reproducibility is an indicator of the agreement between 2 measurements of MCP for the same milk sample preserved with or without addition of Bronopol. Coefficient of repeatability is an indicator of the agreement between repeated measures of MCP. Pearson correlations between MCP measures for NP and PS were 0.97 and 0.83 for RCT and a(30), respectively. Reproducibility of CRM measures under different preserving conditions of milk was 93.5% for RCT and 64.6% for a(30). Repeatabilities of RCT and a(30) measures were 95.7 and 77.3%, respectively. Based on the estimated cross-validation standard errors and coefficients of determination and ratios of standard errors of cross-validation to standard deviation of reference data, the predictive ability of MIRS calibration equations was moderate for RCT and unsatisfactory for a(30.) Predictive ability of equations based on spectra and MCP measures of PS was greater than that of equations based on data of NP. The study did not provide conclusive evidence on the effectiveness of MIRS as a predictive tool for MCP and it requires an enlargement of the variability of milk sampling circumstances. Because the relevance of MIRS predictions in relation to breeding programs for MCP based on indicator traits relies on the genetic variation of MIRS predictions and on phenotypic and genetic correlations between MIRS predictions and MCP measures, additional specific investigations on these topics are needed.

Genetic response for milk production traits, somatic cell score, acidity and coagulation properties in Italian Holstein–Friesian population under current and alternative selection indices and breeding objectives

“…Direct measures were assumed to be recorded in laboratory by computerized renneting meter as described by Dal Zotto et al (2008), whereas predicted measures by MIRS . The indices were of the form…”

Effectiveness of mid-infrared spectroscopy to predict fatty acid composition of Brown Swiss bovine milk

“…Its potential has also been demonstrated to predict coagulation properties of bovine milk (Dal Zotto et al, 2008;De Marchi et al, 2009b), detail milk protein composition (De Marchi et al, 2009a) and -E-mail: mauro.penasa@unipd.it acetone content for the detection of subclinical ketosis (Heuer et al, 2001). Soyeurt et al (2006) and Rutten et al (2009) demonstrated the accuracy of the prediction of FA composition using MIR, but only for the most representative FA.…”

Standardization of milk mid-infrared spectrometers for the transfer and use of multiple models

“…Equations based on the full spectrum have been developed for the determination of fine milk components such as fatty acid profiles (Soyeurt et al, 2006;Rutten et al, 2009), protein composition (Bonfatti et al, 2011), minerals (Soyeurt et al, 2009), ketone bodies (van Knegsel et al, 2010), citrate (Grelet et al, 2016), and lactoferrin (Soyeurt et al, 2007). Other studies have focused on FT-MIR spectra to build equations predicting technological properties of milk such as milk acidity ), ability to coagulate, firmness of curd, or cheese yield (Dal Zotto et al, 2008;Colinet et al, 2015). Recent work has directly considered the FT-MIR spectrum of milk as a reflection of cows' status, with FT-MIR equations being developed to predict methane emissions of dairy cows (Dehareng et al, 2012;Vanlierde et al, 2016), likelihood of conception (Hempstalk et al, 2015), body energy status , energy intake and efficiency (McParland et al, 2014).…”