Prediction of individual milk proteins including free amino acids in bovine milk using mid-infrared spectroscopy and their correlations with milk processing characteristics

Abstract

The aim of this study was to evaluate the effectiveness of mid-infrared spectroscopy in predicting milk protein and free amino acid (FAA) composition in bovine milk. Milk samples were collected from 7 Irish research herds and represented cows from a range of breeds, parities, and stages of lactation. Mid-infrared spectral data in the range of 900 to 5,000 cm(-1) were available for 730 milk samples; gold standard methods were used to quantify individual protein fractions and FAA of these samples with a view to predicting these gold standard protein fractions and FAA levels with available mid-infrared spectroscopy data. Separate prediction equations were developed for each trait using partial least squares regression; accuracy of prediction was assessed using both cross validation on a calibration data set (n=400 to 591 samples) and external validation on an independent data set (n=143 to 294 samples). The accuracy of prediction in external validation was the same irrespective of whether undertaken on the entire external validation data set or just within the Holstein-Friesian breed. The strongest coefficient of correlation obtained for protein fractions in external validation was 0.74, 0.69, and 0.67 for total casein, total β-lactoglobulin, and β-casein, respectively. Total proteins (i.e., total casein, total whey, and total lactoglobulin) were predicted with greater accuracy then their respective component traits; prediction accuracy using the infrared spectrum was superior to prediction using just milk protein concentration. Weak to moderate prediction accuracies were observed for FAA. The greatest coefficient of correlation in both cross validation and external validation was for Gly (0.75), indicating a moderate accuracy of prediction. Overall, the FAA prediction models overpredicted the gold standard values. Near-unity correlations existed between total casein and β-casein irrespective of whether the traits were based on the gold standard (0.92) or mid-infrared spectroscopy predictions (0.95). Weaker correlations among FAA were observed than the correlations among the protein fractions. Pearson correlations between gold standard protein fractions and the milk processing characteristics of rennet coagulation time, curd firming time, curd firmness, heat coagulating time, pH, and casein micelle size were weak to moderate and ranged from -0.48 (protein and pH) to 0.50 (total casein and a30). Pearson correlations between gold standard FAA and these milk processing characteristics were also weak to moderate and ranged from -0.60 (Val and pH) to 0.49 (Val and K20). Results from this study indicate that mid-infrared spectroscopy has the potential to predict protein fractions and some FAA in milk at a population level.

Quantitative analysis of amino acids in human and bovine colostrum milk samples through iTRAQ labeling

“…No significant difference in recovery rate between the two quality control amino acids was identified between samples, and we observed minimal system error during detection, suggesting that the results were reliable. A previous study developed and validated the use of HPLC as a rapid method for the measurement of amino acids in milk, and a recent study reported the efficacy of mid‐infrared spectroscopy for predicting individual milk proteins, including free amino acids, in bovine milk at the population level . Another study used ultra‐performance liquid chromatography MS/MS to determine free amino acid concentrations in breast milk, but was only able to identify nine essential amino acids and 11 nonessential amino acids .…”

“…A previous study developed and validated the use of HPLC as a rapid method for the measurement of amino acids in milk, 24 and a recent study reported the efficacy of mid-infrared spectroscopy for predicting individual milk proteins, including free amino acids, in bovine milk at the population level. 25 Another study used ultra-performance liquid chromatography MS/MS to determine free amino acid concentrations in breast milk, but was only able to identify nine essential amino acids and 11 nonessential amino acids. 26 In addition, precolumn derivatization using diethyl ethoxymethylenemalonate and HPLC was performed to determine 18 amino acids in mare's milk, 27,28 and reversed-phase HPLC was used to determine 20 free amino acids in pasteurized goat-milk cheeses.…”

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

“…In the present study, both milk minerals and MCP were normally distributed, and thus no data transformation was needed. Reference outliers were defined as samples whose values deviated more than 3 SD from the mean of each trait (McDermott et al, 2016a). Based on this definition, none observations for a 30 and K, 1 observation for RCT, k 20 , and Mg, 2 observations for Na and P, and 3 observations for Ca were discarded before the development of prediction models.…”

Strategies for noise reduction and standardization of milk mid-infrared spectra from dairy cattle

“…De Marchi et al (2014) comprehensively reviewed the use of Fourier-transform mid-infrared (FTIR) spectroscopy and many potential applications for the use of the resulting spectra as a phenotyping tool. Ongoing research includes studies of individual milk proteins and fatty acids (Lopez-Villalobos et al, 2014;McDermott et al, 2016;Bonfatti et al, 2017b) and technological properties (Cecchinato et al, 2015;Toffanin et al, 2015;Visentin et al, 2015). Studies have predicted indirect traits related to pregnancy (Lainé et al, 2017;Toledo-Alvarado et al, 2018a,b), energy status Grelet et al, 2016;Mehtiö et al, 2018), efficiency Shetty et al, 2017), and methane emissions (Vanlierde et al, 2013(Vanlierde et al, , 2015Bittante and Cipolat-Gotet, 2018).…”