Measurement of total nitrogen by Kjeldahl analysis is the historical reference method for determination of the protein content of dairy products and is used for both calibration and validation of alternative methods for protein determination. Accurate evaluation of alternative methods is not possible if there is large uncertainty regarding the reference values. When Kjeldahl analysis is used to establish reference values, the performance of the Kjeldahl testing must be verified and within established expectations. Advice is given for Kjeldahl system optimization, evaluation of test results, and trouble-shooting. Techniques for successful Kjeldahl nitrogen analysis of dairy products other than milk are discussed.
Abstract The objective of the survey was to determine if poor homogenizer performance causes nonlinear behavior of the uncorrected fat A or fat B signal that is not detected when an instrument’s residual nonlinearity is determined by using dilutions of homogenized milk instead of unhomogenized milk. Unhomogenized and homogenized (17238 kPa) portions of the same 6.1% fat milk were tested on 20 instruments to determine homogenization efficiency. Instruments with differences of ≥0.087% fat between homogenized and unhomogenized portions of the same milk had inefficient homogenization, on the basis of criteria established in a previous study. Four and 12 instruments out of 20 demonstrated inefficient homogenization for the fat A and fat B channels, respectively. Uncorrected signal linearity for the fat channels was evaluated quantitatively by using a series of dilutions of homogenized (17238 kPa) and unhomogenized milks. Most instruments passed the linearity evaluation for dilutions of either homogenized or unhomogenized milk, even though many of the same instruments failed the homogenization efficiency evaluation. Thus, using dilutions of homogenized milk is valid for linearity evaluation of instruments being used for testing unhomogenized milk in the range of fat concentrations used for payment testing.
ABSTRACT The effect of fat and pH on the best estimate threshold (BET) of three prominent dairy product flavor compounds with varying physicochemical properties: diacetyl (2, 3‐butanedione), δ‐decalactone and furaneol (2,5‐dimethyl‐4‐hydroxy‐3[2H]‐furanone), in water, oil and oil‐in‐water model emulsions (at 10 and 20% fat at neutral and acidified pH 5.5) were investigated. The headspace‐matrix partition coefficients (K HS/matrix ) of each compound in the different matrixes were established using gas chromatography–mass spectrometry. The particle size of the emulsions was controlled. Fat had the largest impact on the BET and partition coefficients of δ‐decalactone followed by diacetyl ( P ≤ 0.05). Fat content did not affect the BET value of furaneol ( P > 0.05) but some effects on partition coefficients were noted ( P ≤ 0.05). BET values of the three compounds were unaffected by pH ( P > 0.05), but differences in partition coefficients ( P ≤ 0.05) were noted for diacetyl and furaneol. PRACTICAL APPLICATIONS This manuscript provides a better understanding of sensory detection thresholds as a result of partitioning of three flavor compounds that are different in physico‐chemical properties and are prominent in dairy product flavor. The acquired knowledge on these compounds may assist product developers in adjusting levels of flavor compounds in reduced fat products to achieve products similar in flavor properties to full fat products, considering the effect of fat and pH of the products on the compounds. Understanding the partition coefficients and detection threshold of one of the compounds studied, diacetyl, may also provide insights in ongoing debates on diacetyl and its safety levels in dairy products.
Cows undergo immense physiological stress to produce milk during early lactation. Monitoring early lactation milk through Fourier-transform infrared (FTIR) spectroscopy might offer an understanding of which cows transition successfully. Daily patterns of milk constituents in early lactation have yet to be reported continuously, and the study objective was to initially describe these patterns for cows of varying parity groups from 3 through 10 d postpartum, piloted on a single dairy. We enrolled 1,024 Holstein cows from a commercial dairy farm in Cayuga County, New York, in an observational study, with a total of 306 parity 1 cows, 274 parity 2 cows, and 444 parity ≥3 cows. Cows were sampled once daily, Monday through Friday, via proportional milk samplers, and milk was stored at 4°C until analysis using FTIR. Estimated constituents included anhydrous lactose, true protein, and fat (g/100 g of milk); relative % (rel%) of total fatty acids (FA) and concentration (g/100 g of milk) of de novo, mixed, and preformed FA; individual fatty acids C16:0, C18:0, and C18:1 cis-9 (g/100 g of milk); milk urea nitrogen (MUN; mg/100 g of milk); and milk acetone (mACE), milk β-hydroxybutyrate (mBHB), and milk-predicted blood nonesterified fatty acids (mpbNEFA) (all expressed in mmol/L). Differences between parity groups were assessed using repeated-measures ANOVA. Milk yield per milking differed over time between 3 and 10 DIM and averaged 8.7, 13.3, and 13.3 kg for parity 1, 2, and ≥3 cows, respectively. Parity differences were found for % anhydrous lactose, % fat, and preformed FA (g/100 g of milk). Parity differed across DIM for % true protein, de novo FA (rel% and g/100 g of milk), mixed FA (rel% and g/100 g of milk), preformed FA rel%, C16:0, C18:0, C18:1 cis-9, MUN, mACE, mBHB, and mpbNEFA. Parity 1 cows had less true protein and greater fat percentages than parity 2 and ≥3 cows (% true protein: 3.52, 3.76, 3.81; % fat: 5.55, 4.69, 4.95, for parity 1, 2, ≥3, respectively). De novo and mixed FA rel% were reduced and preformed FA rel% were increased in primiparous compared with parity 2 and ≥3 cows. The increase in preformed FA rel% in primiparous cows agreed with milk markers of energy deficit, such that mpbNEFA, mBHB, and mACE were greatest in parity 1 cows followed by parity ≥3 cows, with parity 2 cows having the lowest concentrations. When measuring milk constituents with FTIR, these results suggest it is critical to account for parity for the majority of estimated milk constituents. We acknowledge the limitation that this study was conducted on a single farm; however, if FTIR technology is to be used as a method of identifying cows maladapted to lactation, understanding variations in early lactation milk constituents is a crucial first step in the practical adoption of this technology.
Abstract High levels of urea in blood, milk and urine have been linked to poor nitrogen efficiency, increased feed costs, poor reproductive performance and increased environmental impacts of dairy farming. Milk urea nitrogen (MUN) is a commonly used metric to manage herd nitrogen efficiency, with current recommendations for MUN to be between 8–14 mg/dL to maintain milk production and reduce nitrogen losses. However, a previous work suggests commercial analysis of MUN with mid-infrared spectroscopy (MIR) may not be precise enough to determine if a milk sample is within the recommended range. Thus, the objective of this study was to evaluate the precision and accuracy of milk testing lab MUN measurements. Four sets of bulk tank samples were sent to 3 commercial labs and one research lab for analysis by MIR. Samples were sent to commercial labs in duplicate and MUN was also assessed through an enzymatic assay. The Euclidean distance (ED) was calculated as a combined metric of precision and accuracy. The ED was not different between labs and ranged from 0.81–1.27. Repeatability (sr) and reproducibility (sR) were estimated for commercial labs and ranged from (0.297–0.469) and (0.555–0.791) respectively. Differences between individual sample MIR and enzymatic MUN were regressed on the centered enzymatic MUN in a linear mixed model that included a random effect of lab and fixed effects for milk protein and milk fat. Regression results indicate MIR analysis over-predicts MUN at low MUN concentrations and under predicts MUN at high MUN concentrations. Results suggest MIR analysis of MUN is more accurate around milk MUN, protein, and fat concentrations of ~13 mg/dl, 3.4% protein, and 4.2% fat. Further, the combined residual error and random effect of lab suggest the standard error of an MUN MIR measurement is ±1.8.
