Prediction of fatty acid chain length and unsaturation of milk fat by mid-infrared milk analysis

Abstract

Our objective was to develop partial least squares (PLS) models to predict fatty acid chain length and total unsaturation of milk fat directly from a mid-infrared (MIR) spectra of milk at 40°C and then determine the feasibility of using those measures as correction factors to improve the accuracy of milk fat determination. A set of 268 milks (modified milks, farm bulk tank milks, and individual cow) were analyzed for fat, true protein, and anhydrous lactose with chemical reference methods, and in addition a MIR absorption spectra was collected for each milk. Fat was extracted from another portion of each milk, the fat was saponified to produce free fatty acids, and the free fatty acids were converted to methyl esters and quantified using gas-liquid chromatography. The PLS models for predicting the average chain length (carbons per fatty acid) and unsaturation (double bonds per fatty acid) of fatty acids in the fat portion of a milk sample from a MIR milk spectra were developed and validated. The validation performance of the prediction model for chain length and unsaturation had a relative standard deviation of 0.43 and 3.3%, respectively. These measures are unique in that they are fat concentration independent characteristics of fat structure that were predicted directly with transmission MIR analysis of milk. Next, the real-time data output from the MIR spectrophotometer for fatty acid chain length and unsaturation of milk were used to correct the fat A (C=O stretch) and fat B (C-H stretch) measures to improve accuracy of fat prediction. The accuracy validation was done over a period of 5 mo with 12 sets of 10 individual farm milks that were not a part of the PLS modeling population. The correction of a traditional fat B virtual filter result (C-H stretch) for sample-to-sample variation in unsaturation reduced the Euclidean distance for predicted fat from 0.034 to 0.025. The correction of a traditional fat A virtual filter result (C=O stretch) modified with additional information on sample-to-sample variation of chain length and unsaturation gave the largest improvement (reduced Euclidean distance from 0.072 to 0.016) and the best validation accuracy (i.e., lowest Euclidean distance) of all the fat prediction methods.

Effects of rumen-protected methionine on lactation performance and physiological variables during a heat stress challenge in lactating Holstein cows

“…Milk samples were analyzed for fat, true protein, MUN, lactose, TS, total casein, and for SCC using mid-infrared procedures (AOAC International, 1995b). Additionally, milk samples were sent to a commercial laboratory (ADM Laboratories, Clovis, NM) and analyzed for concentration of de novo fatty acids (FA), preformed FA, and mixed FA using mid-infrared procedures as described by Wojciechowski and Barbano (2016).…”

Prediction of milk fatty acid content with mid-infrared spectroscopy in Canadian dairy cattle using differently distributed model development sets

“…Also, importantly, the sample set used to develop the prediction equations needs to incorporate all of the variation expected to be in the population to be predicted. Wojciechowski and Barbano (2016) attained sample variation for the development of PLS models for fatty acid chain length and unsaturation by including bulk tank milk from individual herds in different regions, individual cow milk samples at different lactation stages, and modified milk calibration samples. They achieved R cv 2 of 0.78 and 0.90 for average chain length and unsaturation, respectively.…”

Comparison of Fatty Acid Proportions Determined by Mid-Infrared Spectroscopy and Gas Chromatography in Bulk and Individual Milk Samples

“…As compared to literature results, there are high correlation coefficients between routine and reference methods in individual or bulk milk samples [ 6 , 16 , 17 , 28 , 32 , 33 ]. The similar ratio (higher r in FT-MIR than in the GC method) is valid when comparing the FT-MIR and FT-NIR methods [ 6 ].…”

“…These analyzers are used in dairy laboratories with different types of modifications according to the analytical techniques of the manufacturers (Foss Electric from Denmark, Bentley Instruments from the USA, Delta Instruments from the Netherlands, and others). Recently, in addition to the basic components [ 2 , 7 ] of milk (fat, protein, casein, lactose, total solids, solids-non-fat), the analysis spectrum has been expanding, primarily with undesirable metabolites (citric acid, free fatty acids (FAs)), ketones (acetone, beta-hydroxybutyric acid), urea [ 5 , 10 , 11 , 12 , 13 , 14 ], and other components in milk, especially the milk FA proportion (MFAP) [ 3 , 15 , 16 , 17 ]. The monitoring of the above-listed milk components and metabolites serve to control the health, breeding, nutrition, and reproduction of cows as well as the quality of their milk [ 5 , 12 , 16 , 18 , 19 , 20 , 21 , 22 ].…”