Quantification of bovine milk protein composition and coagulation properties using infrared spectroscopy and chemometrics: A result of collinearity among reference variables

Abstract

Predicting protein fractions and coagulation properties in bovine milk using Fourier transform infrared (FT-IR) measurements is desirable. However, such predictions may rely on correlations with total protein content. The aim of this study was to show how correlations between total protein content, protein fractions, and coagulation properties are responsible for the successful prediction of protein fractions and rennet-induced coagulation properties in milk samples. This study comprised 832 bovine milk samples from 2 breeds (426 Holstein and 406 Jersey). Holstein samples were collected from 20 Danish dairy herds from October to December 2009; Jersey samples were collected from 22 Danish dairy herds from February to April 2010. All samples were from conventional herds and taken while cows were housed. The results showed that κ-CN, αS1-CN, αS1-CN with 8 phosphorylated groups attached (αS1-CN 8P), and curd firming rate could be predicted from FT-IR measurements of the milk samples (with coefficients of determination between 0.66 and 0.71). However, the success of these FT-IR-based predictions was based on indirect relationships with total protein content. Hence, the FT-IR predictions relied on covariance structures with total protein content rather than absorption bands directly associated with the protein fractions and coagulation properties. If covariance structures between the protein fractions, coagulation properties, and total protein content used to calibrate partial least squares models were not conserved in future samples, these samples would show incorrect predictions of the protein fractions and coagulation properties. We demonstrated this using samples from 1 breed to calibrate and samples from the other breed to validate partial least squares models for β-CN. The 2 breeds had different covariance structures between β-CN and total protein content, and the validation samples yielded incorrect predictions. This finding may limit the usefulness of FT-IR-based predictions of protein fractions in milk recording, because indirect covariance structures in the calibration set must be valid for future samples, or future samples will show incorrect predictions.

Genome-wide association study on Fourier transform infrared milk spectra for two Danish dairy cattle breeds

“…Breed has an effect on milk composition [24,45], FT-IR milk spectra [5,23], and the heritability of FT-IR milk spectra [17,20,21,46]. In the current study more QTL were observed for Danish Jersey than for Danish Holstein (Table 1).…”

“…Cattle breed influences milk composition [19,[22][23][24], and the genetic architecture of milk composition [25][26][27]. These breed differences in milk composition are reflected in the FT-IR milk spectra.…”

Genetic analysis of orotic acid predicted with Fourier transform infrared milk spectra

“…This indirect information could be linked to major milk components. Detailed milk composition, for example, depended on indirect information hidden in covariance structures related to major milk components (Eskildsen et al, 2014(Eskildsen et al, , 2016. With the standardization method used in the current study, the standard deviations of individual wavenumbers were altered, which could have disrupted covariance structures that were initially used as an information source by the unstandardized prediction model.…”

Visualizing indirect correlations when predicting fatty acid composition from near infrared spectroscopy measurements

“…reference variables or sample properties) have only received minor attention. [6][7][8][9] This study discusses issues of regression modeling when strong covariance structures exist in the reference data and how to visualize (diagnose) when these covariance structures become dominating during regression modeling.…”