Short communication: Predictive ability of Fourier-transform mid-infrared spectroscopy to assess CSN genotypes and detailed protein composition of buffalo milk

Abstract

The aim of this work was to test the applicability of Fourier-transform mid-infrared spectroscopy (FT-MIR) for the prediction of the contents of casein (CN) and whey protein fractions in buffalo milk. Buffalo milk samples spectra were collected using a MilkoScan FT2 (Foss, Hillerød, Denmark) over the spectral range from 5,000 to 900 wavenumber × cm(-1). Contents of protein fractions, as well as CSN1S1 and CSN3 genotypes, were assessed by reversed phase HPLC. The highest coefficients of determination in cross-validation (1 - VR) were obtained for the contents (g/L of milk) of total protein and CN (1 - VR=0.92), followed by the content of β-CN, total whey protein, and αS2-CN (1 - VR of 0.87, 0.77, and 0.63, respectively). Conversely, contents of αS1-CN, γ-CN, glycosylated-κ-CN, total κ-CN, and whey protein fractions were predicted with poor accuracy (1 - VR <0.51). When protein fractions were expressed as percentages to total protein, 1 - VR values were never greater than 0.61 (β-CN). Only 56 and 70% of the observations were correctly classified by discriminant analysis in each of 2 groups of CSN1S1 and CSN3 genotypes, respectively. Results showed that FT-MIR spectroscopy is not applicable when prediction of detailed milk protein composition with high accuracy is required. Predictions may play a role as indicator traits in selective breeding, if the genetic correlation between FT-MIR predictions and measures of milk protein composition are high enough and predictions of protein fraction contents are sufficiently independent from the predicted total protein content.

Short communication: Variations in major mineral contents of Mediterranean buffalo milk and application of Fourier-transform infrared spectroscopy for their prediction

“…Variations in major mineral contents of Mediterranean buffalo milk and application of Fouriertransform infrared spectroscopy for their prediction G. Stocco,* C. Cipolat-Gotet,* V. Bonfatti, † S. Schiavon,* G. Bittante,* and A. Cecchinato* 1 8681 cently, Bonfatti et al (2015) investigated the ability of FTIR spectroscopy to predict the protein composition of buffalo milk, but so far no studies have investigated the possibility of predicting major mineral contents. The aims of our study were (1) to assess variability in the major mineral components of buffalo milk, (2) to estimate the effect of certain environmental sources of variation on the major minerals during lactation, and (3) to investigate the possibility of using FTIR spectroscopy for indirect prediction of the mineral content of individual buffalo milk samples.…”

Fourier‐transform infrared spectroscopy for soil organic matter analysis

“…This issue is sometimes termed the “cage” of covariance (Eskildsen et al., 2021). In compositionally complex samples, covariance structure itself is often the major driver of chemometric predictions (Eskildsen et al., 2016), necessitating a sufficiently representative sample set to ensure this covariance structure in prediction models for accurate application to new samples (Bonfatti et al., 2015; Eskildsen et al., 2014; Huang et al., 2021). While there is no set rule for the sample number required for a calibration set, the large amount of data in FTIR spectra can result in erroneous and misleading prediction quality if the calibration set is too small.…”

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

“…Eskildsen et al (2014) have argued that successful prediction of individual fatty acids in milk by FT-IR measurement is due to covariance between individual fatty acids and total fat content. Bonfatti et al (2015) have speculated that similar covariance between protein fractions and TP content is responsible for the successful prediction of protein fractions. Total protein can be easily predicted from FT-IR measurements using the amide II band (Luinge et al, 1993), and good correlations between TP and individual protein fractions might enable indirect predictions of protein fractions using the amide II band.…”