Impact of fatty acid composition on the accuracy of mid-infrared fat analysis of farm milks

Our objective was to determine whether data from a previous study using model milk emulsions to characterize the influence of variation in fatty acid chain length and unsaturation on mid-infrared (MIR) fat predictions could be used to identify a strategy to improve the accuracy of MIR fat predictions on a population of farm milks with a wide variation in fatty acid chain length and unsaturation. The mean fatty acid chain length for 45 farm milks was 14.417 carbons, and the mean unsaturation was 0.337 double bonds per fatty acid. The range of fatty acid chain lengths across the 45 farm milks was 1.23 carbons, and the range in unsaturation was 0.167 double bonds per fatty acid. Fat B (absorbance by the carbon-hydrogen stretch) MIR predictions increased and fat A MIR (absorbance by the ester carbonyl stretch) predictions decreased relative to reference chemistry with increasing fatty acid chain length. When the fat B MIR fat predictions were corrected for sample-to-sample variation in unsaturation, the positive correlation between fat B and fatty acid chain length increased from a coefficient of determination of 0.42 to 0.89. A 45:55 ratio of fat B corrected for unsaturation and fat A gave a smaller standard deviation of the difference between MIR prediction and reference chemistry than any ratio of the fat B (without correction for unsaturation) and fat A or either fat B or fat A alone. This demonstrates the technical feasibility of this approach to improve MIR testing accuracy for fat, if a simple procedure could be developed to determine the unsaturation of fat in milk rapidly and to correct the fat B reading for the effect of unsaturation before being combined with fat A.     

Modified versus producer milk calibration: mid-infrared analyzer performance validation

Our objective was to determine the validation performance of mid-infrared (MIR) milk analyzers, using the traditional fixed-filter approach, when the instruments were calibrated with producer milk calibration samples vs. modified milk calibration samples. Ten MIR analyzers were calibrated using producer milk calibration sample sets, and 9 MIR milk analyzers were calibrated using modified milk sample sets. Three sets of 12 validation milk samples with all-laboratory mean chemistry reference values were tested during a 3-mo period. Calibration of MIR milk analyzers using modified milk increased the accuracy (i.e., better agreement with chemistry) and improved agreement between laboratories on validation milk samples compared with MIR analyzers calibrated with producer milk samples. Calibration of MIR analyzers using modified milk samples reduced overall mean Euclidian distance for all components for all 3 validation sets by at least 24% compared with MIR analyzers calibrated with producer milk sets. Calibration with modified milk sets reduced the average Euclidian distance from all-laboratory mean reference chemistry on validation samples by 40, 25, 36, and 27%, respectively for fat, anhydrous lactose, true protein, and total solids. Between-laboratory agreement was evaluated using reproducibility standard deviation (s_R). The number of single Grubbs statistical outliers in the validation data was much higher (53 vs. 7) for the instruments calibrated with producer milk than for instruments calibrated with modified milk sets. The s_R for instruments calibrated with producer milks (with statistical outliers removed) was similar to data collected in recent proficiency studies, whereas the s_R for instruments calibrated with modified milks was lower than those calibrated with producer milks by 46, 52, 61, and 55%, respectively for fat, anhydrous lactose, true protein, and total solids.     

Effect of microbiological testing on subsequent mid-infrared milk component analysis of the same milk sample

Our objectives were to determine if mixing and sampling of a raw milk sample at 4°C for determination of total bacteria count (TBC) and if incubation at 14°C for 18 h and sampling for a preliminary incubation (PI) count influenced the accuracy of subsequent fat, protein, or lactose measurement by mid-infrared (IR) analysis of milk from the same sample container due to either nonrepresentative sampling or the presence of microbial metabolites produced by microbial growth in the milk from the incubation. Milks of 4 fat levels (2.2, 3, 4, and 5%) reflected the range of fat levels encountered in producer milks. If the portion of milk removed from a cold sample was not representative, then the effect on a milk component test would likely be larger as fat content increases. Within the milks at each fat level, 3 treatments were used: (1) 20 vials of the same milk sampled for testing TBC using a BactoScan FC and then used for a milk component test; (2) 20 vials for testing TBC plus PI count followed by component test; and (3) 20 vials to run for IR component test without a prior micro sampling and testing. This was repeated in 3 different weeks using a different batch of milk each week. No large effect on the accuracy of component milk testing [IR fat B (carbon hydrogen stretch) and fat A (carbonyl stretch)] due to the cold milk sample handling and mixing procedures used for TBC was detected, confirming the fact that the physical removal of milk from the vial by the BactoScan FC (Foss Electric, Hillerød, Denmark) was a representative portion of the milk. However, the representativeness of any other sampling procedure (manual or automated) of a cold milk sample before running milk component testing on the same container of milk should be demonstrated and verified periodically as a matter of routine laboratory quality assurance. Running TBC with a BactoScan FC first and then IR milk analysis after had a minimal effect on milk component tests by IR when milk bacteria counts were within pasteurized milk ordinance limits of <100,000 cfu/mL. Running raw milk PI counts (18 h of incubation at 13–14°C) with the BactoScan FC before milk component testing by IR milk analysis had an effect on component tests. The effect was largest on fat test results and would decrease the accuracy of milk payment testing on individual producer milks. The effect was most likely due to the absorption of light by bacterial metabolites resulting from microbial growth or other chemical degradation processes occurring in the milk during the PI count incubation, not by the sampling procedure of the BactoScan. The direction of the effect on component test results will vary depending on the bacteria count and the type of bacteria that grew in the milk, and this could be different in every individual producer milk sample.     

Lipolysis and proteolysis of modified and producer milks used for calibration of mid-infrared milk analyzers

Our objective was to determine if lipolysis or proteolysis of calibration sets during shelf life influenced the mid-infrared (MIR) readings or calibration slopes and intercepts. The lipolytic and proteolytic deterioration was measured for 3 modified milk and 3 producer milk calibration sets during storage at 4°C. Modified and producer milk sets were used separately to calibrate an optical filter and virtual filter MIR analyzer. The uncorrected readings and slopes and intercepts of the calibration linear regressions for fat B, fat A, protein, and lactose were determined over 28 d for modified milks and 15 d for producer milks. It was expected that increases in free fatty acid content and decreases in the casein as a percentage of true protein of the calibration milks would have an effect on the MIR uncorrected readings, calibration slopes and intercepts, and MIR predicted readings. However, the influence of lipolysis and proteolysis on uncorrected readings was either not significant, or significant but very small. Likewise, the amount of variation accounted for by day of storage at 4°C of a calibration set on the calibration slopes and intercepts was also very small. Most of the variation in uncorrected readings and calibration slopes and intercepts were due to differences between the optical filter and virtual filter analyzers and differences between the pasteurized modified milk and raw producer milk calibration sets, not due to lipolysis or proteolysis. The combined impact of lipolysis and proteolysis on MIR predicted values was <0.01% in most cases.     

Short communication: Analytical method and amount of preservative added to milk samples may alter milk urea nitrogen measurements

Milk urea N (MUN) is used by dairy nutritionists and producers to monitor dietary protein intake and is indicative of N utilization in lactating dairy cows. Two experiments were conducted to explore discrepancies in MUN results provided by 3 milk processing laboratories using different methods. An additional experiment was conducted to evaluate the effect of 2-bromo-2-nitropropane-1, 3-diol (bronopol) on MUN analysis. In experiment 1, 10 replicates of bulk tank milk samples, collected from the Pennsylvania State University's Dairy Center over 5 consecutive days, were sent to 3 milk processing laboratories in Pennsylvania. Average MUN differed between laboratory A (14.9 ± 0.40 mg/dL; analyzed on MilkoScan 4000; Foss, Hillerød, Denmark), laboratory B (6.5 ± 0.17 mg/dL; MilkoScan FT + 6000), and laboratory C (7.4 ± 0.36 mg/dL; MilkoScan 6000). In experiment 2, milk samples were spiked with urea at 0 (7.3 to 15.0 mg/dL, depending on the laboratory analyzing the samples), 17.2, 34.2, and 51.5 mg/dL of milk. Two 35-mL samples from each urea level were sent to the 3 laboratories used in experiment 1. Average analyzed MUN was greater than predicted (calculated for each laboratory based on the control; 0 mg of added urea): for laboratory A (23.2 vs. 21.0 mg/dL), laboratory B (18.0 vs. 13.3 mg/dL), and laboratory C (20.6 vs. 15.2 mg/dL). In experiment 3, replicated milk samples were preserved with 0 to 1.35 mg of bronopol/mL of milk and submitted to one milk processing laboratory that analyzed MUN using 2 different methods. Milk samples with increasing amounts of bronopol ranged in MUN concentration from 7.7 to 11.9 mg/dL and from 9.0 to 9.3 mg/dL when analyzed on MilkoScan 4000 or CL 10 (EuroChem, Moscow, Russia), respectively. In conclusion, measured MUN concentrations varied due to analytical procedure used by milk processing laboratories and were affected by the amount of bronopol used to preserve milk sample, when milk was analyzed using a mid-infrared analyzer. Thus, it is important to maintain consistency in milk sample preservation and analysis to ensure precision of MUN results.     

Prediction of blood metabolites from milk mid-infrared spectra in early-lactation cows

Dairy cows commonly experience an unbalanced energy status in early lactation, and this condition can lead to the onset of several metabolic disorders. Blood metabolic profile testing is a valid tool to monitor and detect the most common early lactation disorders, but blood sampling and analysis are time-consuming and expensive, and the procedure is invasive and stressful for the cows. Mid-infrared (MIR) spectroscopy is routinely used to analyze milk composition, being a cost-effective and nondestructive method. The present study aimed to assess the feasibility of using routine milk MIR spectra for the prediction of main blood metabolites in dairy cows, and to investigate associations between measured blood metabolites and milk traits. Twenty herds of Holstein Friesian, Brown Swiss, or Simmental cows located in Northeast Italy were visited 1 to 4 times between December 2017 and June 2018, and blood and milk samples were collected from all lactating cows within 35 d in milk. Concentrations of main blood metabolites and milk MIR spectra were recorded from 295 blood and milk samples and used to develop prediction models for blood metabolic traits through backward interval partial least squares analysis. Blood β-hydroxybutyrate (BHB), urea, and nonesterified fatty acids were the most predictable traits, with coefficients of determination of 0.63, 0.58, and 0.52, respectively. On the contrary, predictive performance for blood glucose, triglycerides, cholesterol, glutamic oxaloacetic transaminase, and glutamic pyruvic transaminase were not accurate. Associations of blood BHB and urea with their respective contents in milk were moderate to strong, whereas all other correlations were weak. Predicted blood BHB showed an improved performance in detecting cows with hyperketonemia (blood BHB ≥ 1.2 mmol/L), compared with commercial calibration equation for milk BHB. Results highlighted the opportunity of using milk MIR spectra to predict blood metabolites and thus to collect routine information on the metabolic status of early-lactation cows at a population level.     

不同保鲜剂处理对山药贮藏品质的影响

研究了在温度18～20℃、相对湿度40%环境下,1-MCP(1-甲基环丙烯)、TBZ(特克多)、壳聚糖等保鲜剂处理对山药品质的影响。每20d测定样品的多酚氧化酶(PPO)酶活性、总酚含量、丙二醛(MDA)含量、相对电导率、硬度、可溶性固形物(SSC)等指标。结果表明,壳聚糖涂膜处理对山药储藏的效果最佳,PPO、总酚、MDA、相对电导率、SSC、硬度等指标均优于其他处理。贮藏至第120d,衰老指标MDA、相对电导率分别为0.68μmol/L、35.97%,对照为0.95μmol/L、38.79%。1-MCP和TBZ处理对山药保鲜也有一定作用,但效果不如壳聚糖。     

Evaluation of the MilkoScan FT 6000 milk analyzer for determining the freezing point of goat's milk under different analytical conditions

The aim of this research was to evaluate the Milko-Scan FT 6000 (Foss Electric, Hillerød, Denmark) for determining the freezing point (FP) of goat's milk under different analytical conditions. The FP was determined in duplicate in 1,800 milk aliquots obtained from 45 bulk tank milk samples from 10 Murciano-Granadina goat herds, using the MilkoScan method and a reference thermistor cryoscopy method (Advanced Instrument Inc., Norwood, MA). Five different preservation strategies—no preservative, preservation with azidiol (0.006 or 0.018 g of sodium azide/100 mL), and preservation with bronopol (0.020 or 0.040 g/100 mL)—were then used to preserve the milk. For each preservation strategy, 8 different amounts of water were added (0, 1, 2, 3, 4, 5, 6, or 7% total volume). The results obtained with each method under these 40 analytical conditions were examined by comparison of means, comparison of the standard deviations of repeatability (s_r and its relative value s_r%), and a regression analysis. Under most analytical conditions, the FP was recorded as lower by the MilkoScan method, with a mean difference of 1.5 m°C compared with the reference method. Both methods showed similar repeatabilities (the overall s_r% was 0.22% for the MilkoScan method and 0.20% for the reference method). In comparisons of the 2 methods, the highest regression coefficients were obtained with aliquots containing >3% added water. The best regression coefficients (0.85 to 1.02) were obtained for milk samples preserved with bronopol at 0.020 g/100 mL. These results allow the MilkoScan method to be used with goat's milk for screening purposes. The factors of added water, preservative, analytical method, lactose concentration, and the effect of the bulk tank milk sample within each lactose group contributed significantly to the observed variation in FP. For practical purposes, either of the bronopol concentrations could be used when determining the FP of goat's milk with the methods tested. However, the increase in the concentration of sodium azide in the azidiol formula contributed to an important reduction in the FP recorded. Thus, the type and concentration of preservative should be taken into account when interpreting FP values.     

复合保鲜剂对分割生鲜鸡肉保鲜效果的优化

为了提高熟化肉鸡的保质期,以对生鲜鸡肉中的常见致病菌（志贺氏菌、沙门氏菌和金黄色葡萄球菌）和腐败菌（假单胞菌）的抑菌圈大小作为评价指标,通过单因素和L9（34）正交实验,确定复合保鲜剂的最佳配比为Nisin浓度为0.03%,双乙酸钠浓度为0.25%,虾壳提取液浓度为0.3%。经复合保鲜剂处理的鸡肉,感官评分、蒸煮损失、离心损失率、挥发性盐基氮（TVB-N）值和菌落总数的实验结果均优于空白对照组和化学保鲜组（山梨酸钾和脱氢醋酸钠）,在4℃冷藏条件下能使鸡肉的保质期比对照延长5⁶d,比化学保鲜剂延长3⁴d。      

Calibration of infrared milk analyzers: modified milk versus producer milk

Mid-infrared (MIR) milk analyzers are traditionally calibrated using sets of preserved raw individual producer milk samples. The goal of this study was to determine if the use of sets of preserved pasteurized modified milks improved calibration performance of MIR milk analyzers compared with calibration sets of producer milks. The preserved pasteurized modified milk sets exhibited more consistent day-to-day and set-to-set calibration slope and intercept values for all components compared with the preserved raw producer milk calibration sets. Pasteurized modified milk calibration samples achieved smaller confidence interval (CI) around the regression line (i.e., calibration uncertainty). Use of modified milk calibration sets with a larger component range, more even distribution of component concentrations within the ranges, and the lower correlation of fat and protein concentrations than producer milk calibration sets produced a smaller 95% CI for the regression line due to the elimination of moderate and high leverage samples. The CI for the producer calibration sets were about 2 to 12 times greater than the CI for the modified milk calibration sets, depending on the component. Modified milk calibration samples have the potential to produce MIR milk analyzer calibrations that will perform better in validation checks than producer milk-based calibrations by reducing the mean difference and standard deviation of the difference between instrument values and reference chemistry.     

Genetic variability of milk components based on mid-infrared spectral data

The aim of this study was to estimate the genetic parameters of the mid-infrared (MIR) milk spectrum represented by 1,060 data points per sample. The dimensionality of traits was reduced by principal components analysis. Therefore, 46 principal components describing 99.03% of the phenotypic variability were used to create 46 new traits. Variance components were estimated using canonical transformation. Heritability ranged from 0 to 0.35. Twenty-five out of 46 studied traits showed a permanent environment variance greater than genetic variance. Eight traits showed heritability greater than 0.10. Variances of original spectral traits were obtained by back transformation. Heritabilities for each spectral data points ranged from 0.003 to 0.42. In particular, 3 MIR regions showing moderate to high heritability estimates were of potential genetic interest. Heritabilities for specific wave numbers, linked with common milk traits (e.g., lipids, lactose), were similar to those estimated for these traits. This research confirms the genetic variability of the MIR milk spectrum and, therefore, the genetic variation of milk components. The objective of this study was to better understand the genetics of milk composition and, maybe in the future, to select animals to improve milk quality.     

Determination of protein concentration in raw milk by mid-infrared fourier transform infrared/attenuated total reflectance spectroscopy

This study investigates the potential use of attenuated total reflectance spectroscopy in the mid-infrared range for determining protein concentration in raw cow milk. The determination of protein concentration is based on the characteristic absorbance of milk proteins, which includes 2 absorbance bands in the 1500 to 1700 cm(-1) range, known as the amide I and amide II bands, and absorbance in the 1060 to 1100 cm(-1) range, which is associated with phosphate groups covalently bound to casein proteins. To minimize the influence of the strong water band (centered around 1640 cm(-1)) that overlaps with the amide I and amide II bands, an optimized automatic procedure for accurate water subtraction was applied. Following water subtraction, the spectra were analyzed by 3 methods, namely simple band integration, partial least squares (PLS) and neural networks. For the neural network models, the spectra were first decomposed by principal component analysis (PCA), and the neural network inputs were the spectra principal components scores. In addition, the concentrations of 2 constituents expected to interact with the protein (i.e., fat and lactose) were also used as inputs. These approaches were tested with 235 spectra of standardized raw milk samples, corresponding to 26 protein concentrations in the 2.47 to 3.90% (weight per volume) range. The simple integration method led to very poor results, whereas PLS resulted in prediction errors of about 0.22% protein. The neural network approach led to prediction errors of 0.20% protein when based on PCA scores only, and 0.08% protein when lactose and fat concentrations were also included in the model. These results indicate the potential usefulness of Fourier transform infrared/attenuated total reflectance spectroscopy for rapid, possibly online, determination of protein concentration in raw milk.     

乳成分对酸奶凝胶微观结构和流变学性质的影响

酸奶凝胶的许多宏观的物理特性与其微观结构和流变学性质密切相关。从酸奶的微结构、流变学性质和质地等方面综述了乳脂肪、蛋白质及调节酪蛋白和乳清蛋白比例对酸奶凝胶的影响。     

Short communication: Effect of storage and preservation on total bacterial counts determined by automated flow cytometry in bulk tank goat milk

This study was designed to evaluate the effects of different storage conditions on total bacterial count (TBC) determinations made in goat bulk tank milk using an automated flow cytometry method. The storage conditions tested were storage temperature (refrigeration at 4 and 10°C or freezing at −20°C), the use of a preservative (no preservative, NP; azidiol, AZ; or bronopol, BR), and the age of the milk samples for each analytical condition (storage times at 4°C: from 0 h to 5 d for NP; and from 0 h to 22 d for AZ and BR; storage times at 10°C: from 24 h to 2 d for NP and from 24 h to 22 for AZ and BR; storage times at −20°C: from 24 h to 22 d for NP, AZ, and BR). Significant effects on individual bacterial count (IBC) variation were shown by the bulk tank milk sample, preservative, storage temperature, interaction preservative × storage temperature, and milk age within the interaction preservative × storage temperature. In preserved samples, the highest IBC were obtained for AZ and the lowest counts were obtained in samples preserved with BR. Because of the variation in IBC recorded in BR-preserved samples, we recommend that BR should not be used for TBC determinations using the automated flow cytometry method. The NP samples stored at 4 and 10°C showed significantly higher IBC at 24 h postcollection, also invalidating these analytical conditions for TBC analyses. The practical implications of our findings are that goat milk samples preserved with AZ and stored at 10 or 4°C are appropriate for TBC by the BactoScan flow cytometry method for up to 24 h and 11 d postcollection, respectively.     

Effect of preservatives on milk composition determination

The main objective of our study was to determine the effect of different concentration of four different preservatives, potassium dichromate, Azidiol, Bronopol and Microtabs II, on the results of laboratory analyses of milk using mid-infrared spectrometry. The final concentrations of the preservatives in the raw cows' milk samples were 0.005%, 0.01%, 0.05%, 0.1%, 0.5% and 1% respectively. We analysed unpreserved, fresh bulk raw cows' milk 2 and 24 h after milking. The effect of preservatives on the composition of the milk was studied 24 h after preservation. The experiment was replicated 10 times. Each experiment was carried out after the calibration of the mid-infrared spectrophotometer using calibration samples preserved with 0.02% Bronopol. We found that the concentration of the preserving agent had a significant effect (P < 0.005) on the results of the laboratory analyses; therefore, the proper concentration of preservative has to be used for routine sample preservation.     

复合防腐剂对袋装萝卜干防腐效果的比较研究

通过对萧山萝卜干中微生物的初步分析、鉴定,确定其微生物主要为蜡样芽孢杆菌。通过防腐剂抑菌实验,得出苯甲酸双乙酸钠复合防腐剂为一种可用于萝卜干防腐的高效防腐剂。     

Short communication: Prediction of milk coagulation and acidity traits in Mediterranean buffalo milk using Fourier-transform mid-infrared spectroscopy

Milk coagulation and acidity traits are important factors to inform the cheesemaking process. Those traits have been deeply studied in bovine milk, whereas scarce information is available for buffalo milk. However, the dairy industry is interested in a method to determine milk coagulation and acidity features quickly and in a cost-effective manner, which could be provided by Fourier-transform mid-infrared (FT-MIR) spectroscopy. The aim of this study was to evaluate the potential of FT-MIR to predict coagulation and acidity traits of Mediterranean buffalo milk. A total of 654 records from 36 herds located in central Italy with information on milk yield, somatic cell score, milk chemical composition, milk acidity [pH, titratable acidity (TA)], and milk coagulation properties (rennet coagulation time, curd firming time, and curd firmness) were available for statistical analysis. Reference measures of milk acidity and coagulation properties were matched with milk spectral information, and FT-MIR prediction models were built using partial least squares regression. The data set was divided into a calibration set (75%) and a validation set (25%). The capacity of FT-MIR spectroscopy to correctly classify milk samples based on their renneting ability was evaluated by a canonical discriminant analysis. Average values for milk coagulation traits were 13.32 min, 3.24 min, and 39.27 mm for rennet coagulation time, curd firming time, and curd firmness, respectively. Milk acidity traits averaged 6.66 (pH) and 7.22 Soxhlet-Henkel degrees/100 mL (TA). All milk coagulation and acidity traits, except for pH, had high variability (17 to 46%). Prediction models of coagulation traits were moderately to scarcely accurate, whereas the coefficients of determination of external validation were 0.76 and 0.66 for pH and TA, respectively. Canonical discriminant analysis indicated that information on milk coagulating ability is present in the MIR spectra, and the model correctly classified as noncoagulating the 91.57 and 67.86% of milk samples in the calibration and validation sets, respectively. In conclusion, our results can be relevant to the dairy industry to classify buffalo milk samples before processing.     

Comparison of analytical methods and the influence of milk components on milk urea nitrogen recovery

The objectives of this study were to compare analytical instruments used in independent laboratories to measure milk urea nitrogen (MUN) and determine whether any components in milk affect the recovery of MUN. Milk samples were collected from 100 Holstein cows fed one ration in a commercial dairy herd with a rolling herd average of 9500 kg. Half of each sample was spiked with 4 mg/dL of urea N, while the other half was not, to determine recovery. Both milk samples (spiked and not spiked) were sent to 14 independent laboratories involved in the MUN Quality Control Program through National Dairy Herd Improvement Association and analyzed for MUN, fat, protein, lactose, somatic cell count (SCC), and total solids. The laboratories analyzed MUN using CL-10 (n = 3), Skalar (n = 2), Bentley (n = 3), Foss 4000 (n = 3) or Foss 6000 (n = 3) systems. When recovery of MUN was evaluated among the 5 analytical methods, the mean recoveries for the Bentley, Foss 6000, and Skalar systems were 92.1 (SE = 2.76%), 95.4 (SE = 10.1%), and 95.1% (SE = 7.61%), respectively, and did not differ from each other. However, MUN recovery was 85.0% (SE = 2.8%) for the CL-10 system and 47.1% (SE = 9.9%) for the Foss 4000 system, both of which differed from the other 3 systems. Recoveries from Foss 4000, Foss 6000, and Skalar varied among laboratories using the same instrument. As initial MUN concentration increased, recovery decreased using the Bentley and CL-10 systems. Increasing milk fat resulted in a decrease in recovery using the Foss 6000 system. For 4 of the 5 methods, recovery of MUN was not associated with specific milk components. Recovery of MUN was inconsistent for laboratories using the Foss 4000 and the Foss 6000 method and using these systems may result in an overestimation or underestimation of MUN.     

Assessing the effect of pregnancy stage on milk composition of dairy cows using mid-infrared spectra

Changes in milk production traits (i.e., milk yield, fat, and protein contents) with the pregnancy stage are well documented. To our knowledge, the effect of pregnancy on the detailed milk composition has not been studied so far. The mid-infrared (MIR) spectrum reflects the detailed composition of a milk sample and is obtained by a nonexhaustive and widely used method for milk analysis. Therefore, this study aimed to investigate the effect of pregnancy on milk MIR spectrum in addition to milk production traits (milk yield, fat, and protein contents). A model including regression on the number of days pregnant was applied on milk production traits (milk yield, fat, and protein contents) and on 212 spectral points from the MIR spectra of 9,757 primiparous Holstein cows from Walloon herds. Effects of pregnancy stage were expressed on a relative scale (effect divided by the squared root of the phenotypic variance); this allowed comparisons between effects on milk traits and on 212 spectral points. Effect of pregnancy stage on production traits were in line with previous studies indicating that the model accounted well for the pregnancy effect. Trends of the relative effect of the pregnancy stage on the 212 spectral points were consistent with known and observed effect on milk traits. The highest effect of the pregnancy was observed in the MIR spectral region from 968 to 1,577 cm^?1. For some specific wavenumbers, the effect was higher than for fat and protein contents in the beginning of the pregnancy (from 30 to 90 or 120 d pregnant). In conclusion, the effect of early pregnancy can be observed in the detailed milk composition through the analysis of the MIR spectrum of bovine milk. Further analyses are warranted to explore deeply the use of MIR spectra of bovine milk for breeding and management of dairy cow pregnancy.