Infrared Milk Analyzer Research Articles

Effect of carbohydrate level in the diets of Kholmogor cows on milk productivity and milk quality

The effect of the diets with different ratio of easily digestible carbohydrates on milk productivity and milk quality of high-yielding Kholmogor cows during the milking period was studied. The research and production experiment on the cows was conducted in the Arkhangelsk region in 2022. Selection of animals in the control and experimental groups was carried out by the method of pair-analogues under the stall barn system. Two groups of cows participated in the research: experimental and control groups of 10 cows each. The experiment lasted for 100 days. Carbohydrate-protein mixture was used as a carbohydrate supplement to the diets of experimental animals in the amount of 1.5 kg per head per day. Selective milk quality control for mass fraction of fat, protein, lactose, dry matter, milk solids non-fat (MSNF), freezing point, urea and somatic cells were determined on a Bentley Combi 150 high-speed infrared milk analyzer at the Selective Milk Quality Control Laboratory. It was found that the sugar-protein ratio in the experimental group was twice higher (0.58) than in the control group (0.29), due to the presence of carbohydrate supplementation in the diet. The increase in milk productivity of the animals by 4.6 and 0.14% of the protein level in milk at inclusion of carbohydrate-protein mixture in the amount of 1.5 kg per one head per day in the diet was established. A 3.2% decrease in the level of urea in milk was recorded, indicating a more efficient use of amino acids by the animals in the process of metabolism at the same level of absorption of ammonium forms while increasing the level of simple carbohydrates in the diet. The positive effect of this additive on the content of dry skim milk residue, which increased in the milk of the cows of the experimental group by 0.128 absolute % (abs.%), was revealed. Application of the carbohydrate-protein mixture in the amount of 1.5 kg per one head per day and control over the quality of produced products will increase the milk productivity of cows, improve the quality of products, increase the productive longevity of dairy cattle.

Evaluation of Factors Affecting Colostrum Quality and Quantity in Holstein Dairy Cattle.

Simple SummaryAdministration of high-quality colostrum is vital for short and long-term health of newborn calves. It ensures the passive transfer of immunity and covers the high energy requirements during the first hours of calf’s life. Colostrum composition and factors affecting it have not been evaluated in large scale studies. Several factors affecting colostrum quality and yield were revealed. The digital BRIX refractometer proved quite useful. Colostrum quality can be maximized by management practices accounting for factors affecting its composition.The objective of this study was to conduct a large-scale investigation of colostrum composition and yield and an evaluation of factors affecting them. In this study, 1017 clinically healthy Holstein cows from 10 farms were used. The colostrum TS were measured using a digital Brix refractometer. Fat, protein and lactose content were determined using an infrared Milk Analyzer. Statistical analysis was conducted using a series of univariate general linear models. The mean (±SD) percentage of colostrum fat, protein, lactose and TS content were 6.37 (3.33), 17.83 (3.97), 2.15 (0.73) and 25.80 (4.68), respectively. Parity had a significant positive effect on the protein and TS content and a negative one on fat content. The time interval between calving and colostrum collection had a significant negative effect on the fat, protein and TS contents and a positive one on lactose. Colostrum yield had a significant negative effect on the protein and TS content, and it was affected by all factors considered. In addition to TS, the evaluation of the colostrum fat content appears essential when neonates’ energy needs are considered. The Brix refractometer, an inexpensive and easy to use devise, can be used effectively in colostrum quality monitoring.

Genetic parameters of colostrum traits in Holstein dairy cows

The main objective of this study was to assess the genetic background of colostrum yield and quality traits after calving in Holstein dairy cows. The secondary objective was to investigate genetic and phenotypic correlations among laboratory-based and on-farm-measured colostrum traits. The study was conducted in 10 commercial dairy herds located in northern Greece. A total of 1,074 healthy Holstein cows with detailed pedigree information were examined from February 2015 to September 2016. All cows were clinically examined on the day of calving and scored for body condition. All 4 quarters were machine-milked, and a representative and composite colostrum sample was collected and examined. Colostrum total solids (TS) content was determined on-farm using a digital Brix refractometer. Colostrum fat, protein, and lactose contents were determined using an infrared milk analyzer, and energy content was calculated using National Research Council (2001) equations. Dry period length (for cows of parity ≥2), milk yield of previous 305-d lactation (for cows of parity ≥2), age at calving, parity number, season of calving, time interval between calving and first colostrum milking, and milk yield were recorded. Each trait (colostrum yield and quality traits) was analyzed with a univariate mixed model, including fixed effects of previously mentioned factors and the random animal additive genetic effect. All available pedigrees were included in the analysis, bringing the total animal number to 5,662. Estimates of (co)variance components were used to calculate heritability for each trait. Correlations among colostrum traits were estimated with bivariate analysis using the same model. Mean percentage (±SD) colostrum TS, fat, protein, and lactose contents were 25.8 ± 4.7, 6.4 ± 3.3, 17.8 ± 4.0, and 2.2 ± 0.7%, respectively; mean energy content was 1.35 ± 0.3 Mcal/kg and mean colostrum yield was 6.18 ± 3.77 kg. Heritability estimates for the above colostrum traits were 0.27, 0.21, 0.19, 0.15, 0.22, and 0.04, respectively. Several significant genetic and phenotypic correlations were derived. The genetic correlation of TS content measured on-farm with colostrum protein was practically unity, whereas the correlation with energy content was moderate (0.61). Fat content had no genetic correlation with TS content; their phenotypic correlation was positive and low. Colostrum yield was not correlated genetically with any of the other traits. In conclusion, colostrum quality traits are heritable and can be amended with genetic selection.

Prediction of Milk Coagulation Properties and Individual Cheese Yield in Sheep Using Partial Least Squares Regression

Simple SummaryConsidered that all sheep milk in Italy is destined for cheese processing, traits describing rennet coagulation aptitude should be among the most important selection goals for dairy breeds. To reduce the costs and logistics related to the large-scale recording of these traits, mid-infrared (MIR) spectroscopy could be conveniently used to generate reliable predictions without any additional cost. The aims of this research were to predict the milk coagulation properties (MCP) and individual cheese yield (ILCY) in sheep by MIR spectrometry using partial least squares regression (PLS), and to compare different data pre-treatment procedures. The prediction results observed in the present study, although moderate, suggest the possibility of adding novel phenotypes (e.g., MCP and ILCY) in breeding schemes for dairy sheep breeds. Mid-infrared spectroscopy coupled with PLS regression could allow the prediction of phenotypes at the population level without additional costs.The objectives of this study were (i) the prediction of sheep milk coagulation properties (MCP) and individual laboratory cheese yield (ILCY) from mid-infrared (MIR) spectra by using partial least squares (PLS) regression, and (ii) the comparison of different data pre-treatments on prediction accuracy. Individual milk samples of 970 Sarda breed ewes were analyzed for rennet coagulation time (RCT), curd-firming time (k20), and curd firmness (a30) using the Formagraph instrument; ILCY was measured by micro-manufacturing assays. An Furier-transform Infrared (FTIR) milk-analyzer was used for the estimation of the milk gross composition and the recording of MIR spectrum. The dataset (n = 859, after the exclusion of 111 noncoagulating samples) was divided into two sub-datasets: the data of 700 ewes were used to estimate prediction model parameters, and the data of 159 ewes were used to validate the model. Four prediction scenarios were compared in the validation, differing for the use of whole or reduced MIR spectrum and the use of raw or corrected data (locally weighted scatterplot smoothing). PLS prediction statistics were moderate. The use of the reduced MIR spectrum yielded the best results for the considered traits, whereas the data correction improved the prediction ability only when the whole MIR spectrum was used. In conclusion, PLS achieves good accuracy of prediction, in particular for ILCY and RCT, and it may enable increasing the number of traits to be included in breeding programs for dairy sheep without additional costs and logistics.

Prediction of sheep milk chemical composition using milk yield, pH, electrical conductivity and refractive index.

This Research Communication addresses the hypothesis that fat, protein, lactose and total solids content can be predicted using daily milk yield (DMY), pH, electrical conductivity (MEC) and refractive index (RI) of milk as predictors. It also addresses the possibility of these measurements being used for on-farm benchmarking activities towards selecting the highest yielding animals and flocks regarding milk quality traits (MQT). A total of 308 purebred Frizarta ewes were used for the study. From each individual ewe, a composite milk sample was collected. pH, MEC and RI of milk were measured and the samples were assayed for fat, protein, lactose and total solids content, using an automatic infrared milk analyser. The predictive value of DMY, pH, MEC and RI of milk on its MQT was assessed using multiple linear regression analysis. Significant regression equations were produced for all of the studied traits. RI and MEC were significant and reliable predictors for all studied MQT, whereas DMY was a significant predictor for most MQT with the exception of protein content. pH was a marginally significant predictor for some of the MQTs at the initial development of the equations but proved unreliable after bootstraping. Using these equations a number of ewes varying from 75 (for fat) to 97 (for protein) out of the 100 highest MQT yielders were correctly predicted, whereas, none of the ewes out of the 100 lowest MQT yielders was mispredicted as a high yielder for protein-, lactose- and total solids- content. Three out of 100 lowest fat-yielders were mispredicted as high fat-yielders. Similar equations can be used for benchmarking activities towards selecting the highest protein-, fat-, lactose- and total solids- yielding animals and flocks in cases where laboratories for MQT analyses are not readily available or the cost of chemical analyses is high. The method can be regarded as a handy tool for the dairy industry to readily assess milk quality at the farm level.

Infrared analyzers for breast milk analysis: fat levels can influence the accuracy of protein measurements.

Currently, there is a growing interest in lacto-engineering in the neonatal intensive care unit, using infrared milk analyzers to rapidly measure the macronutrient content in breast milk before processing and feeding it to preterm infants. However, there is an overlap in the spectral information of different macronutrients, so they can potentially impact the robustness of the measurement. In this study, we investigate whether the measurement of protein is dependent on the levels of fat present while using an infrared milk analyzer. Breast milk samples (n=25) were measured for fat and protein content before and after being completely defatted by centrifugation, using chemical reference methods and near-infrared milk analyzer (Unity SpectraStar) with two different calibration algorithms provided by the manufacturer (released 2009 and 2015). While the protein content remained unchanged, as measured by elemental analysis, measurements by infrared milk analyzer show a difference in protein measurements dependent on fat content; high fat content can lead to falsely high protein content. This difference is less pronounced when measured using the more recent calibration algorithm. Milk analyzer users must be cautious of their devices' measurements, especially if they are changing the matrix of breast milk using more advanced lacto-engineering.

A proficiency test system to improve performance of milk analysis methods and produce reference values for component calibration samples for infrared milk analysis

Our goal was to determine the feasibility of combining proficiency testing, analytical method quality-assurance system, and production of reference samples for calibration of infrared milk analyzers to achieve a more efficient use of resources and reduce costs while maximizing analytical accuracy within and among milk payment-testing laboratories. To achieve this, we developed and demonstrated a multilaboratory combined proficiency testing and analytical method quality-assurance system as an approach to evaluate and improve the analytical performance of methods. A set of modified milks was developed and optimized to serve multiple purposes (i.e., proficiency testing, quality-assurance and method improvement, and to provide reference materials for calibration of secondary testing methods). Over a period of years, the approach has enabled the group of laboratories to document improved analytical performance (i.e., reduced within- and between-laboratory variation) of chemical reference methods used as the primary reference for calibration of high-speed electronic milk-testing equipment. An annual meeting of the laboratory technicians allows for review of results and discussion of each method and provides a forum for communication of experience and techniques that are of value to new analysts in the group. The monthly proficiency testing sample exchanges have the added benefit of producing all-laboratory mean reference values for a set of 14 milks that can be used for calibration, evaluation, and troubleshooting of calibration adjustment issues on infrared milk analyzers.

Rapid measurement of macronutrients in breast milk: How reliable are infrared milk analyzers?

SUMMARYBackground & aimsSignificant biological variation in macronutrient content of breast milk is an important barrier that needs to be overcome to meet nutritional needs of preterm infants. To analyze macronutrient content, commercial infrared milk analyzers have been proposed as efficient and practical tools in terms of efficiency and practicality. Since milk analyzers were originally developed for the dairy industry, they must be validated using a significant number of human milk samples that represent the broad range of variation in macronutrient content in preterm and term milk. Aim of this study was to validate two milk analyzers for breast milk analysis with reference methods and to determine an effective sample pretreatment. Current evidence for the influence of (i) aliquoting, (ii) storage time and (iii) temperature, and (iv) vessel wall adsorption on stability and availability of macronutrients in frozen breast milk is reviewed.MethodsBreast milk samples (n = 1188) were collected from 63 mothers of preterm and term infants. Milk analyzers: (A) Near-infrared milk analyzer (Unity SpectraStar, USA) and (B) Mid-infrared milk analyzer (Miris, Sweden) were compared to reference methods, e.g. ether extraction, elemental analysis, and UPLC-MS/MS for fat, protein, and lactose, respectively.ResultsFor fat analysis, (A) measured precisely but not accurately (y = 0.55x + 1.25, r2 = 0.85), whereas (B) measured precisely and accurately (y = 0.93x + 0.18, r2 = 0.86). For protein analysis, (A) was precise but not accurate (y = 0.55x + 0.54, r2 = 0.67) while (B) was both precise and accurate (y = 0.78x + 0.05, r2 = 0.73). For lactose analysis, both devices (A) and (B) showed two distinct concentration levels and measured therefore neither accurately nor precisely (y = 0.02x + 5.69, r2 = 0.01 and y = −0.09x + 6.62, r2 = 0.02 respectively). Macronutrient levels were unchanged in two independent samples of stored breast milk (−20 °C measured with IR; −80 °C measured with wet chemistry) over a period of 14 months.ConclusionsMilk analyzers in the current configuration have the potential to be introduced in clinical routine to measure fat and protein content, but will need major adjustments.

Fat content, energy value and fatty acid profile of donkey milk during lactation and implications for human nutrition

Background and aimsMilk contains numerous nutrients. The content of n-3 fatty acids, the n-6/n-3 ratio, and short- and medium-chain fatty acids may promote positive health effects. In Western societies, cow’s milk fat is perceived as a risk factor for health because it is a source of a high fraction of saturated fatty acids. Recently, there has been increasing interest in donkey’s milk. In this work, the fat and energetic value and acidic composition of donkey’s milk, with reference to human nutrition, and their variations during lactation, were investigated. We also discuss the implications of the acidic profile of donkey’s milk on human nutrition.MethodsIndividual milk samples from lactating jennies were collected 15, 30, 45, 60, 90, 120, 150, 180 and 210days after foaling, for the analysis of fat, proteins and lactose, which was achieved using an infrared milk analyser, and fatty acids composition by gas chromatography.ResultsThe donkey’s milk was characterised by low fat and energetic (1719.2kJ·kg-1) values, a high polyunsaturated fatty acids (PUFA) content of mainly α-linolenic acid (ALA) and linoleic acid (LA), a low n-6 to n-3 FA ratio or LA/ALA ratio, and advantageous values of atherogenic and thrombogenic indices. Among the minor PUFA, docosahesaenoic (DHA), eicosapentanoic (EPA), and arachidonic (AA) acids were present in very small amounts (<1%). In addition, the AA/EPA ratio was low (0.18). The fat and energetic values decreased (P < 0.01) during lactation.The fatty acid patterns were affected by the lactation stage and showed a decrease (P < 0.01) in saturated fatty acids content and an increase (P < 0.01) in the unsaturated fatty acids content. The n-6 to n-3 ratio and the LA/ALA ratio were approximately 2:1, with values <1 during the last period of lactation, suggesting the more optimal use of milk during this period.ConclusionsThe high level of unsaturated/saturated fatty acids and PUFA-n3 content and the low n-6/n-3 ratio suggest the use of donkey’s milk as a functional food for human nutrition and its potential utilisation for infant nutrition as well as adult diets, particular for the elderly.

Partial demineralization and concentration of acid whey by nanofiltration combined with diafiltration

In this paper, nanofiltration (NF) combined with continuous variable volume diafiltration (CVVD) of cottage cheese whey (also known as acid whey) was investigated to simultaneously concentrate and partially demineralize it. The experiments were carried out in a laboratory NF equipment using a flat sheet membrane with the effective area of 0.046 m 2 and 96% sucrose rejection. Fat, protein, lactose and total solid content of the samples were analyzed by Infrared Milk Analyzer Bently 150. Ion composition of the samples was determined by ICP-MS instrument. The concentrations were carried out at different values of the ratio of the volume of dialyzation water and the volume of permeate ( α). The degree of demineralization of monovalent ions reached 70% and 90% at 2.5 volume concentration ratio, and at diafiltration and permeate volume ratios of 0.5 and 0.75, respectively. The amount of multivalent ions increased continuously during the NF combined with CVVD of acid whey. The rejection of the valuable compounds of whey such as protein and lactose was over 90%.

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.

An Empirical Method for Prediction of Cheese Yield

Theoretical cheese yield can be estimated from the milk fat and casein or protein content of milk using classical formulae, such as the VanSlyke formula. These equations are reliable predictors of theoretical or actual yield based on accurately measured milk fat and casein content. Many cheese makers desire to base payment for milk to dairy farmers on the yield of cheese. In small factories, however, accurate measurement of fat and casein content of milk by either chemical methods or infrared milk analysis is too time consuming and expensive. Therefore, an empirical test to predict cheese yield was developed which uses simple equipment (i.e., clinical centrifuge, analytical balance, and forced air oven) to carry out a miniature cheese making, followed by a gravimetric measurement of dry weight yield. A linear regression of calculated theoretical versus dry weight yields for milks of known fat and casein content was calculated. A regression equation of y=1.275x + 1.528, where y is theoretical yield and x is measured dry solids yield (r2=0.981), for Cheddar cheese was developed using milks with a range of theoretical yield from 7 to 11.8%. The standard deviation of the difference (SDD) between theoretical cheese yield and dry solids yield was 0.194 and the coefficient of variation (SDD/mean×100) was 1.95% upon cross validation. For cheeses without a well-established theoretical cheese yield equation, the measured dry weight yields could be directly correlated to the observed yields in the factory; this would more accurately reflect the expected yield performance. Payments for milk based on these measurements would more accurately reflect quality and composition of the milk and the actual average recovery of fat and casein achieved under practical cheese making conditions.

Comparison of goat milk standards with cow milk standards for analyses of somatic cell count, fat and protein in goat milk

Goat milk standards and cow milk standards were used to calibrate a Fossomatic-300 fluorescent cell counter and Dairylab II infrared milk analyzer for analyses of goat milk samples. Somatic cell counts (SCC) of goat milk were 27% lower (5.5 × 10 5 ml − 1 versus 7.0 × 10 5 ml − 1) when the Fossomatic-300 was calibrated with goat milk SCC standards than with cow milk SCC standards ( P < 0.001). When the Dairylab II was calibrated with goat milk component standards, the levels of fat and protein in milk samples were 0.04% and 0.27% higher, respectively, than with cow milk component standards ( P < 0.001). The data indicated that Fossomatic instruments and infrared milk analyzers must be calibrated with goat milk standards for more reliable and accurate analysis of goat milk.

Infrared Analysis of Milk: Effect of Homogenizer and Optical Filter Selection on Apparent Homogenization Efficiency and Repeatability

Abstract The apparent homogenization efficiency (AHE) and repeatability of one instrument was measured with 5 instrument homogenizers that differ in true homogenization efficiency (THE) to determine if instrument characteristics change when different fat A and B optical filters are used in the same machine. Inefficient homogenizers demonstrated both poor repeatability and poor AHE with both sets of fat B filters. One set of fat A filters produced infrared readings that were influenced less by differences in homogenization efficiencies than the other set. The most inefficient homogenizer demonstrated poor repeatability and failed AHE evaluation with both pairs of fat B filters and one pair of fat A filters. Differences in the transmission characteristics of the 2 sets of fat A filters influenced results of AHE evaluation for the most inefficient homogenizers. However, a very efficient homogenizer passed evaluation of AHE and demonstrated good repeatability on both unhomogenized and homogenized milks regardless of differences in the spectral characteristics of filters. A second objective was to determine if AHE test values are correlated with true differences in milk fat globule (MFG) size distribution. A good correlation was found between AHE and THE data for a group of homogenizers for one reference milk. A weakness of the AHE tesl is that the MFG size distribution of the externally homogenized reference milk may vary from one evaluation to another, resulting in an inconsistent reference point for AHE values. The preferred method of evaluating homogenization efficiency of infrared milk analyzers is analysis of the MFG size distribution resulting from instrument homogenization of unhomogenized milk.

Effect of Infrared Analyzer Homogenization Efficiency on Repeatability of Uncorrected Fat A and Fat B Signals

Poor repeatability by infrared milk analyzers may be caused by inefficient homogenization as a result of light scattering and the Christiansen effect. The objectives of this study were to identify instruments with good and poor homogenization efficiency and to determine if a difference exists in repeatability performance between instruments with good vs poor homogenization efficiency. Unhomogenized and homogenized portions of the same milk were tested 20 times consecutively on 22 instruments. An instrument was considered to have poor homogenization efficiency if the mean difference in the uncorrected signal between unhomogenized and homogenized portions of the same milk was > or = 1.43% of the fat test (i.e., > or = 0.05% at 3.5% fat). Instruments were evaluated for repeatability by calculating the sample standard deviation and the range of the latter 19 uncorrected readings for unhomogenized and homogenized milks. When repeatability was evaluated as a function of homogenization efficiency, there was a significant (p = 0.001) correlation between poor homogenization efficiency and poor repeatability when testing unhomogenized milk but not when testing homogenized milk. Improved homogenizer performance within infrared milk analyzers is needed to improve the repeatability of raw milk testing.

A Quantitative Linearity Evaluation Method for Infrared Milk Analyzers

Abstract Nonlinearity in the uncorrected signal for any channel of the infrared milk analyzer can cause decreased accuracy of the final corrected result. A procedure quantifying the amount of residual nonlinearity in the uncorrected signal for each channel was developed for use in the range of milk component concentrations appropriate for producer payment testing. For the fat channels, a homogenized 6% fat milk is diluted with water to obtain a series of solutions from 0 to 6% fat. For the protein and lactose channels, evaporated skim milk is diluted with water to obtain linearity solutions from 2.8 to 3.9% true protein and 4.3 to 6.0% anhydrous lactose. The selection of dilution method (i.e., weight/weight or volume/volume) should be based on how the final calibrated instrument values are to be expressed (i.e., weight/weight or weight/volume). A non-zero forcing linear regression is conducted within a chemical range by using known component concentrations as the dependent variable (Y) and uncorrected infrared signals as the independent variable (X). Residual nonlinearity values are calculated. A channel possesses unacceptable nonlinearity if residual values are ≥±0.02% at any point within the component range normally encountered in producer payment testing. The linearity status of 18 instruments was determined.

Performance of Homogenizers in Infrared Milk Analyzers: A Survey

Abstract The objective of this survey was to determine if infrared milk analyzers can achieve a &amp;lt;0.05% difference in fat test between unhomogenized and homogenized portions of the same milk, as stated in official methods. Two batches of pasteurized, unhomogenized milk (3 and 6% fat) were prepared from a single source of raw cream and skim. Homogenized milks were produced at 3 pressures: 0, 10342, and 17238 kPa. Pairs of unhomogenized and homogenized portions of the same milk were tested on 22 infrared analyzers. More instruments exceeded the 0.05% difference between homogenized and unhomogenized milk than expected; differences between homogenized and unhomogenized milk increased with increasing homogenization pressure and fat concentration. At 3% fat, differences between homogenized and unhomogenized milks at both 10342 and 17238 kPa were &amp;lt;0.05% for 21 of 22 instruments for fat A; only 17 and 13 of 22 instruments for fat B achieved a difference of &amp;lt;0.05% at 10342 and 17238 kPa, respectively. At 6% fat, differences between homogenized and unhomogenized milks for fat A were &amp;lt;0.05% for 15 and 14 of 22 instruments at the 2 pressures, respectively. For fat B, 8 and 5 of 22 instruments achieved a difference of &amp;lt;0.05% at 10432 and 17238 kPa. Instruments were also evaluated for homogenization efficiency by a revised criterion that is dependent on the fat content of the samples used for the evaluation.

Crude and Protein Nitrogen Bases for Protein Measurement and Their Impact on Current Testing Accuracy

As the value of milk protein increases, accuracy of measurement of milk protein content becomes more important. Traditionally, milk protein content has been estimated from total N. However, about 5 to 6% of milk total N is not associated with protein and varies among farms, regions, and seasons. Currently, most milk protein testing is done with infrared milk analyzers using Kjeldahl total N as a basis for protein calibration. Analytical errors occur as a result of using total N as the basis for protein calibration of infrared analyzers instead of true protein. These errors are caused by two factors: differences in mean NPN as a percentage of total N from one set of calibration samples to the next and differences in NPN as a percentage of total N between samples within each set of calibration milks. Examples of the magnitude of errors created by these factors are calculated using actual data from industry laboratories. The accuracy of protein testing using infrared analyzers for payment and record-keeping purposes could be improved substantially by using true protein as a basis for calibration instead of total N.

Comparison of Alternative Calibration Procedures for Infra-red Milk Analyzers

Alternative calibration procedures were used to produce instrumental estimates based on reference sample signals and chemical values collected over a period of one year in the Ontario Central Milk Testing Laboratory, Guelph, Ontario, Canada. Mean difference (estimate less chemical values) and standard deviation of differences from selected combinations of calibration conditions were evaluated using two ANOVA models. Model I considered four types of fat calibration [Fat A, Fat B, Fat ApB (73% B and 27% A), and Fat A+B (50% A and 50% B)], three levels of the number of samples between calibration, three levels of the number of samples used for calibration and three instruments. Model II compared the fixed intercorrection factor (FIF) approach to the multiple linear regression (MLR) approach using the same levels of samples for and between calibrations, as in Model I. The results showed that the MLR approach was statistically equivalent to the FIF approach, and Fat ApB (Fat A proportion B) calibration was equivalent to Fat A+B but slightly better than Fat B alone, which was slightly better than Fat A alone. The optimum samples for and between calibrations were 50, and 25 or 50, respectively. In most cases, instrument effects were significant and were similar in magnitude to treatment effects.

Interlaboratory Assessment of Dry Calibration Milk Powders for Calibrating Infrared Milk Analyzers

Abstract An interlaboratory study was carried out to assess the performance of preformulated, preanalyzed, dry calibration milk powders designed for calibrating infrared milk analyzers. The calibration powders can be reconstituted to produce consistent calibrations within laboratories. The powders met AOAC specifications In terms of accuracy and repeatability, and provide calibrations with performance characteristics comparable to those produced with conventional calibration milks. The reconstituted solutions were shown to be stable for up to 6 h at 40°C, and can be stored under refrigerated conditions and used for repeated analyses for up to 21 days without apparent deterioration In calibration performance. In general, the calibration powders perform as well as, or better than, the conventional calibrants used by the laboratories participating in the study, and the calibrations could be switched without significantly changing the analytical results. Simulation and cross-calibration analyses indicated that the powdered calibrants produced more consistent results overall than fluid calibration milks. The powdered calibrants, as formulated, are shown to be suitable for any application requiring calibrations that meet AOAC specifications, Including payment applications. The calibration powders have the stability and performance characteristics to serve as a consistent, stable reference standard for monitoring instrument performance, and would be a useful tool for Interlaboratory standardization or accrediting payment and dairy herd analysis laboratories.