Abstract #M107
Section: Breeding and Genetics
Session: Breeding and Genetics I
Format: Poster
Day/Time: Monday 7:30 AM–9:30 AM
Location: Exhibit Hall B
Session: Breeding and Genetics I
Format: Poster
Day/Time: Monday 7:30 AM–9:30 AM
Location: Exhibit Hall B
# M107
Sources of variation in minor milk components and their potential prediction using mid-infrared spectroscopy.
A. Fleming*1, F. S. Schenkel1, S. Nayeri1, C. Baes1, R. A. Ali2, M. Corredig3,4, F. Miglior1,5, 1Centre for Genetic Improvement of Livestock, Department of Animal Biosciences, University of Guelph, Guelph, ON, Canada, 2Department of Mathematics and Statistics, University of Guelph, Guelph, ON, Canada, 3Department of Food Science, University of Guelph, Guelph, ON, Canada, 4Gay Lea Foods Co-operative, Mississauga, ON, Canada, 5Canadian Dairy Network, Guelph, ON, Canada.
Sources of variation in minor milk components and their potential prediction using mid-infrared spectroscopy.
A. Fleming*1, F. S. Schenkel1, S. Nayeri1, C. Baes1, R. A. Ali2, M. Corredig3,4, F. Miglior1,5, 1Centre for Genetic Improvement of Livestock, Department of Animal Biosciences, University of Guelph, Guelph, ON, Canada, 2Department of Mathematics and Statistics, University of Guelph, Guelph, ON, Canada, 3Department of Food Science, University of Guelph, Guelph, ON, Canada, 4Gay Lea Foods Co-operative, Mississauga, ON, Canada, 5Canadian Dairy Network, Guelph, ON, Canada.
Variation in minor milk components is of interest to consumers and milk processors but additional knowledge is needed to ascertain its sources. A phenotyping tool such as mid-infrared (MIR) spectroscopy would help future inquiry into these traits on a large-scale routine basis. The objectives of this work were to examine the variation of characteristics and contents of minor milk components and assess the ability to predict them from MIR spectra of the milk. A total of 4,116 milk samples were collected from 421 cows representing 4 dairy breeds coming from 47 Canadian herds. The MIR spectra of the milk samples were obtained through DHI labs. Not all samples were analyzed for all traits, but some overlap occurred. Samples were analyzed for casein micelle size (n = 3,117), total (n = 986) and soluble (n = 937) calcium, lactoferrin (n = 2,054), and α-, β-, and κ-casein proportions (n = 2,067). Milk component traits were examined separately using a linear model including the effects of herd nested within breed, days in milk class, parity, season, and animal. Herd nested within breed, days in milk class, and season had significant effects on all examined traits. Significant differences were noted between breeds for casein micelle size, total calcium, lactoferrin, and casein proportions. Lactoferrin and casein proportions also differed between parities. Moderate and negative Pearson correlation coefficients were found for lactoferrin content with test-day milk, fat, and protein yields (P < 0.001). Moderate and positive Pearson correlation coefficients were found between lactoferrin and somatic cell score as well as total calcium and protein percentage (P < 0.001). Partial least squares regression using a 10-fold random subset cross-validation was used to predict the milk components from the MIR spectra. The average cross-validation R2 (R2cv) values were very low for casein micelle size, total and soluble calcium, and α-, β-, and κ-casein proportions at 0.31, 0.24, 0.16, 0.03, 0.10, and 0.08, respectively. For lactoferrin a R2cv of 0.55 was found, which may allow for MIR predicted lactoferrin to serve as a limited indicator of the true lactoferrin content in milk.
