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

Transcription

1 Crude and Protein Nitrogen Bases for Protein Measurement and Their Impact on Current Testing Accuracy DAVID M. BARBANO and JOANNA M. LYNCH Northeast Dalry Foods Research Center Department of Food Science Cornell University Ithaca, NY ABSTRACT 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. (Key words: protein, infrared milk analysis, nonprotein nitrogen) Abbreviation key: NCN = noncasein N, NPN%TN =NPN as a percentage of total N, TN =total N, TP =true protein, TPN =true protein N. Received September 30, Accepted February 10, INTRODUCTION The primary use of bovine milk is for human food. Major nutrients in milk include fat, protein, carbohydrate, Ca, and P. For children, milk fat and carbohydrates are an important source of calories. The protein and mineral contents of milk have emerged as the most attractive milk nutrients for adults concerned about the influence of nutrition on general health. This has resulted in development and increased consumption of many caloriereduced and low fat dairy products during the past 5 yr. The increased sales (10) of low fat dairy products are demonstrated by the dramatic shift from 1973 to 1989 from whole milk to low fat milk (Figure 1). This shift in consumption will continue to be driven by recommendations from health professionals for reduction of total dietary fat intake (1). Changes in dairy product purchasing patterns have increased the value of milk protein and decreased the value of milk fat. These trends are likely to continue. In most areas of the US and in many other countries, the milk payment system is still based on milk weight and fat content. As the commercial value of milk protein continues to increase, it will be necessary to change to a multiple-component pricing system that will include payments to reflect differences in the protein content of various milks. Mid infrared transmittance milk analyzers are the most common type of electronic milk testing equipment used by the dairy industry today for measurement of the fat and protein contents of milk (3). Infrared milk analyzers or any other type of electronic milk testing equipment must be calibrated with samples that have been tested for fat and protein contents by chemical reference methods. The reference method for milk protein determination is the Kjeldahl method for measurement of N. The total N (TN) is multiplied Dairy Sci 75:321Q

2 BORDEN SYMPOSIUM Z 80 0 l/l 70 a: W Q. 60 a: W Q. 50 l/l 40 ::::i: «a: 30 Cl J ILOW FAT I ISKIM I ir- O'-'--~~-~----,---r--~~-~-,.-J YEAR Figure 1. Per capita sales of fluid milks in the US from 1973 to by a factor of 6.38 to express the results on a crude, or TN, protein basis. Historically, the dairy industry has measured the TN content of milk. However, milk TN includes both true protein N (TPN) and NPN. By using TN instead of TPN as a basis for calibration of electronic milk testing equipment, the industry accepts certain limitations in the accuracy of electronic milk protein measurements. The dairy industry needs to understand clearly the magnitude of the inaccuracies caused by using TN instead of TPN as the basis for calibration of electronic milk testing equipment. Once the magnitude of these inaccuracies is defined, the dairy industry must decide whether it is willing to continue to accept these inaccuracies at a time when more accurate alternatives are available. To address this issue, the characteristics of the N distribution in milk will be reviewed. Next, the current status of the KjeldabJ method for measurement of TN and TPN content of milk will be summarized. Finally, the impact of the use of TN- versus TPN-based protein values on the within-laboratory and betweenlaboratory agreement of electronic instruments for measurement of the protein content of milk will be demonstrated using actual data. DISTRIBunON OF N IN MILK Rowland's (12) description of the fractionation of the N-containing compounds in milk has been the basis for characterization of the protein fraction of milk. The most common N fractions measured in milk composition studies are TN, NPN, and noncasein N (NCN). True protein N is calculated as TN minus NPN and casein N as TN minus NCN. Expressed on a protein basis (N x 6.38), TN is referred to as CP, and TPN is referred to as true protein (TP). Both NPN as a percentage of TN (NPN%TN) and casein N as a percentage of TN vary seasonally (2, 5), regionally (2, 5), and among farms (7, 8). Variation in NPN%TN is primarily due to the diet of the dairy cow; casein N, as a percentage of TPN, is influenced by milk quality factors such as sec, endogenous milk protease activity, proteases from psychrotrophic bacteria, and genetic factors. The NPN fraction is composed of urea and other low molecular weight N-containing compounds (e.g., creatine, creatinine, amino acids). Urea accounts for approximately 50% of the N content of the NPN fraction of milk (15, 16), whereas amino acid components are generally <20%. In a recent report, Roseler et al. (11) indicated that much of the diet-induced variation in the NPN content of milk is due to variation in the urea content. When the NPN content of milk increases, it apparently results primarily from an increase in milk urea content. NPN Variation In Farm Milks How much does NPN%TN vary among farms? Variation in NPN%TN has been reported in several studies (7, 8, 13, 14). A study (9) was conducted in 1989 on milks collected at 298 farms from a wide geographic area of the US over a 6-mo period; this study found an average NPN%TN of 5.88% with a range across farms from 3.73 to 7.95%. These results are typical of those reported by investigators in other countries, although the high and low extremes may vary among studies. In general, the low end of the range may be between 2 and 3%, and the high end of the range may be between 8 and 10% for NPN%TN for milk from individual farms. The NPN concentration in milk is not very well correlated with the concentration of TN in milk or with milk quality factors (14) when milk is of normal commercial quality (i.e., Journal of Dairy Science Vol. 75. No

3 3212 BARBANO AND LYNCH TABLE 1. Monthly average regional and seasonal variation in NPN as a percentage of total N (NPN%TN) in the US in 1984 (2).1 Region Month I X Jan Feb Mar Apr May Joo Jul Aug Sep Oct Nov Dec Avg IThe NPN was measured by mixing 5 ml of milk with 5 ml of 24% TCA to obtain a final concentration of 12% TCA. The original method of Rowland (12) uses 10 ml of milk and 40 ml of 15% TCA. Both approaches yield a final concentration of 12% TCA. The former method yields results for NPN%TN that are about 1% lower than those obtained with the original Rowland method, despite the fact that the final concentration of TCA is 12% for both methods. Thus. the data reported in this table are about I % lower than values that would be obtained by the new official method for NPN analysis. total plate count <100,OOO/ml and SCC <1,OOO,OOO/ml). However, there is a slight trend for NPN%TN to decrease with increasing milk protein concentration. This is an important factor later in this discussion. NPN Variation In Bulk Milks How much does NPN%TN vary in bulk milk supplies? The average NPN%TN range in bulk milk is from 4 to 6%. However, this can vary seasonally and regionally. Differences in feed composition and dairy cattle nutrition management practices are likely to be major sources of variation in the NPN%TN content of milks. Seasonal and regional variations for bulk milk supplies within the US (2) are shown in Table 1. Samples were collected 1 d each week from large silos for 1 yr at each of 50 cheese plants in Collectively, these plants utilized approximately 10% of the total milk supply in the US. Because NPN concentration is influenced by feeding, one might also expect some variation among years in NPN content of milk. Based on the data in Table 1, the average NPN%TN for bulk milk can vary among regions of the US by about 1.0%. In addition, NPN%TN of the bulk milk supply within each region can vary seasonally by up to 1.0%. Variations between plants can be even larger than regional differences (2). The impact of these variations in NPN%TN on the accuracy of electronic milk protein measurements needs to be evaluated carefully. CURRENT STATUS OF METHODS TO MEASURE TN AND TPN The Kjeldahl TN method is the reference for milk protein estimation. Recent revisions have been made to the AOAC method for TN measurement, replacing the Hg catalyst with Cu (4). Similar revisions to the International Dairy Federation method are expected. Traditionally, milk TPN is calculated as the difference between TN and NPN, requiring two Kjeldahl analyses per sample. This doubles the number of sample analyses relative to a single TN measurement. Recently, clearly defined Kjeldahl methods for measurement of NPN and TPN were studied collaboratively and approved by the AOAC (6). The NPN method is based on the Rowland procedure (12). Briefly, 40 ml of 15% TCA are added to 10 ml of milk. The milk protein precipitates, the solution is Journal of Dairy Science Vol. 75, No

4 BORDEN SYMPOSIUM 3213 filtered, and the NPN (which is in the filtrate) is measured by Kjeldahl N analysis (6). Many analysts think that it is too much work to measure both TN and NPN to determine TPN. There is also additional analysis cost. Thus, a direct TPN measurement method was developed (6). The direct TPN method uses the Rowland principle for milk sample preparation, but the protein precipitate instead of the filtrate is collected and analyzed for N content by Kjeldahl. The TPN content of milk can be determined directly (instead of by difference) using this method with very little extra work or cost beyond that required for a TN measurement. The direct TPN method has also been collaboratively studied and approved by the AOAC (6). The direct TPN method and the more classical indirect method (TN minus NPN) give equivalent results. The mean values for milk TP for the indirect (2.983%) and the direct methods (2.987%), observed in a collaborative study, were in good agreement (6). The withinlaboratory repeatability and between-laboratory reproducibility of the direct TPN method were better than the repeatability and reproducibility of the indirect TPN method because the indirect method accumulates errors from two separate analyses (Le., TN and NPN). IMPACT OF VARIATION IN NPN ON ACCURACY OF ELECTRONIC MILK TESTING Mid infrared transmission milk analyzers are currently the most common electronic milk testing instruments used for rapid determination of the fat, protein, and lactose contents of milks for payment testing and production record keeping. Infrared instruments base their measurement of milk protein on the absorption of infrared light at ~m by peptide bonds. The NPN fraction of milk absorbs little, if any, light at this wavelength. Thus, in theory, the infrared analyzer would perform best with TPN as a basis for calibration. However, in practice, the dairy industry uses TN-based protein values for calibration of infrared milk analyzers. Use of TN instead of TPN causes analytical errors in protein tests. The source and magnitude of these errors has not been characterized previously. The errors are caused by variation in the NPN%TN of the milk samples used to calibrate infrared instruments. Differences In Mean Calibration Milk NPN%TN The method by which mean differences in NPN%TN from one set of calibration milks to another cause differences in results between instruments is best explained by a simple hypothetical example. Assume that laboratories A and B are using calibration milks with different mean NPN%TN characteristics. The mean values for the calibration milks used by laboratory A are 3.300% CP, 3.135% TP, and 5% NPN%TN; the mean values for laboratory B are 3.335% CP, 3.135% TP, and 6% NPN%TN. The detectors in the infrared instruments in both laboratories only respond to TP, so the signals from the detectors of both instruments are equivalent. Because the CP content of the calibration milks in laboratory A is 3.30, the operator sets the instrument to read an average of 3.30; however, in laboratory B, the operator sets that instrument to read an average of on milk with the same TP content. Thus, if the same milks are tested on both instruments. the average value will be different by about.035% protein because of this factor. Thus, a systematic bias is created between the two laboratories and will be present in all results. In this example, a difference of 1% NPN%TN is used (similar to plant and regional differences). What happens in milk testing laboratories? To determine the magnitude of errors that occur currently in the industry, sets of calibration samples from three different laboratories that make and distribute calibration samples to other laboratories were analyzed. The three laboratories are located in different regions of the country. The data represent one set of calibration milks from each laboratory for each month during The sets of calibration milks from each laboratory contained either 10 or 12 individual milk samples. None of the laboratories selected milk for use in calibration based on the NPN%TN content. The TN and TPN content of each calibration milk within each set was measured by Kjeldahl (4. 6). The average NPN%TN for the total set of calibration milks from each ofthe three laboratories for each month during 1991 are shown in Table 2. Based on the previous example, if the same milk samples were tested on the same instrument in laboratory 3 in May and June, Journal of Dairy Science Vol. 75, No. 11, 1992

5 3214 BARBANO AND LYNCH TABLE 2. Monthly average NPN as a percentage of total N for calibration milks in 1991 from three different laboratories that distribute calibration samples. Laboratory Month 2 3 Range Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Mean Range they would have read differently on average by approximately.04% protein (because of the difference in mean NPN%TN), when the instrument is calibrated based on TN. The same types of errors can be demonstrated across laboratories. If the same milk samples had been tested in laboratories 1 and 2 in March, the average result would have differed by.03 to.04% protein because of this factor. Additional data on NPN%TN were available (data not shown) for laboratory 3 for each month during During that year, the range in average NPN%TN across different sets of calibration milks was 1.4% (from 5.5 to 6.9%). The data in Table 2 indicate clearly that the variation in NPN%TN content of calibration milks among laboratories can create a systematic bias between laboratories that could be as large as.03 to.06% protein for the average of all milks tested. Approximately the same magnitude of bias of the mean test results can exist in the comparison of test values between months within the same laboratory. The magnitudes of these variations in NPN%TN between sets of calibration milks are consistent with seasonal and regional variations in bulk milks demonstrated in Table 1. Given the current methods of evaluation of calibration accuracy commonly used in the dairy industry, it is difficult for laboratory staff to recognize that NPN%TN can be a source of testing error because laboratories are not aware of the variation in the NPN content of their calibration milks. Use of TPN instead of TN as the basis for calibration of these instruments would eliminate any systematic bias in mean protein test results caused by differences in NPN%TN. Variation In NPN%TN Within Each Calibration Set In addition to the variation in mean NPN%TN between sets of calibration milks, there can be even larger variation in NPN%TN between milk samples within a set of calibration milks. The variations among samples for the three different sets of calibration standards in May 1991 produced by each of the three laboratories are shown in Table 3. Variation in NPN%1N within sets of calibration milks is even more important than differences in the mean NPN%TN. Any correlation of NPN%TN as a function of change in protein concentration in the calibration milks for a laboratory will be incorporated into the calibration of the instrument. The change in NPN%TN as a function of TP content of the calibration milks for each laboratory is shown in Figure 2. The values in this figure were calculated by a sim- TABLE 3. Variation in NPN as a percentage of total N among samples within different sets of calibration standards produced by three different laboratories in May Sample number Average Range Laboratory (%) Journal of Dairy Science Vol. 75. No. II, 1992

6 BORDEN SYMPOSIUM 3215 Z I- B~ ' 7 ;fl. 5 Z Q. Z ~.~.. Figure 2. Change in NPN as a percentage of total N (NPN%TN) as a function of true protein content of milk for sets of calibration milks from three different laboratories in May pie nonzero forcing linear regression of NPN%TN as a function of TP content of the milks. The average NPN%TN for laboratories 1 ~d 3 were similar (Le., 6.4 and 6.6; Table 3) 10 May; however, the regression relationship of NPN%TN versus protein shown in Figure 2 for the same milks is quite different for these two laboratories because of the difference in distribution of NPN%TN across the protein percentages. If these relationships are calcu~ lated for each laboratory for each month, monthly variation can be considerable within a laboratory. It is common practice to make slope and bias adjustments of the intercorrected signal on the protein channel of an infrared milk analyzer based on simple regression of instrument readings versus TN values for the calibration samples. Differences in mean NPN%TN between different sets of calibration milks and differences in the correlation between NPN%TN and protein concentration will each cause a separate distortion of results. They may be additive in some cases or may counterbalance each other in other cases. How much difference could exist in the calibration of the protein channel of the instruments in these three laboratories in May 1991 as a result of the variations in NPN%TN within the sets of calibration milks? In this example, each laboratory was assumed to have calibrated its instruments with its own calibration milks, and, then, the calibration milks from laboratory 1 were assumed to have been tested in each of the three laboratories. (It is assumed that all three laboratories could obtain good agreement when testing a common set of milks by Kjeldahl method.) The calculated differences in the protein test results among the three laboratories at various protein percentages that were due to variation in NPN%TN within the calibration sets are shown in Figure 3. The line at 0% protein difference would reflect the comparison of results of laboratory 1 with itself. The line for laboratory 1 versus laboratory 2 indicates that on low protein samples laboratory 2 will test lower than laboratory 1, and on high testing samples laboratory 2 will test higher than laboratory 1. In contrast, test results from laboratory 3 will be similar to results from laboratory 1 at low protein concentrations but will be nearly.1 % higher at high protein concentrations. In practice, it would be extremely difficult for each laboratory to recognize that these systematic differences are occurring, because it would be necessary to test a large group of unknown milks by infrared and by Kjeldahl at each protein concentration to see the trends. A large amount of variation in NPN%TN within the milks at the same TP percentage would create a large amount of scatter around each of the lines as shown in Figure /.10 W..-// () IUB 1 VS.UB3! / z.08 W a: // w.06./ u.. u.../ is.04 ILAB 1 VS.LAB 21 //./ Z iii I-.02.~/ 0 a: a..00 <f L.,-,_..,.-r-r r--,--,.--,--r~,_..,...--r_r._J TRUE PROTEIN PERCENTAGE Figure 3. Calculated between-instrument differences in protein tests among three laboratories when testing the same set of calibration milks during May Differences between laboratories are due to variation in NPN within calibration sets. Journal of Dairy Science Vol. 75, No

7 3216 BARBANO AND LYNCH DISCUSSION It appears that a systematic mean bias of.03 to.06% CP can be created between infrared milk analyzers as a result of differences in mean NPN%1N between sets of calibration milks. In addition, variation in NPN%1N within sets of calibration samples can cause systematic differences as large as.1 % protein at specific protein concentrations. The behavior of one instrument relative to another in CP test results can change every time a new set of calibration milks is used to adjust the calibration. The amount of change will depend on the change in mean NPN%1N and the change in correlation of NPN%1N with protein level from one set of calibration milks to the next. How significant is this variation? The seasonal variation in CP of the milk supply in the US is about.25% (2). The difference in yearly average protein content of milk from one region to another is about.1 % protein (2). An error or bias of.05% protein on infrared milk analyzers that has been created by differences in NPN%1N in calibration milks would represent a high percentage of the seasonal or regional variation in milk protein content. In payment testing, at certain protein levels, the bias differences between payment testing laboratories could be larger than.1 % protein, as explained earlier (Figure 3), and result in inequity in payment among producers both within and between regions. Admittedly, variation NPN%1N is not the only cause of variation in protein test results by infrared milk analyzers. The Kjeldahl method for either 1N or TPN will not give exact agreement between all laboratories. However, the magnitude of difference to be expected between laboratories by Kjeldahl has been documented clearly in collaborative studies (4, 6). In practice, the difference between protein test results from infrared milk analyzers in different laboratories is much greater than can be explained by the variation in Kjeldahl results between laboratories that distribute sets of calibration samples. In our opinion, about half of the difference in mid infrared transmittance protein tests between laboratories using calibration milks from different origins may be due to variation among laboratories in Kjeldahl results, and half may be due to variation in the NPN%1N content of calibration milks when 1N is used as the basis of calibration. As the value of milk protein increases and as differences in milk protein concentration are used for the basis for payment of farmers, the need to eliminate NPN%1N as a source of error in electronic milk testing will become even more important. If instruments were calibrated based on TPN instead of TN, these systematic errors in payment testing and production record-keeping data caused by variation in NPN could be eliminated. One of the arguments against changing to TPN as the basis for protein testing is that TP values are about 6% lower (i.e., 3.3% on CP basis vs. 3.1 % on TP basis) than CP values, and this would lower the protein test of milk. This shift in level would create a need for adjustments in record keeping, statistical data analysis, and possibly nutritional labels on dairy foods. There are at least two different simple approaches to deal with this problem. One would be to adjust all historical data to a lower value by subtracting a constant (e.g.,.2%). This would correct a long-standing error in protein tests. Another approach would be to calibrate infrared analyzers based on TPN and then, in the instrument calibration, to adjust all protein tests upward by a constant (e.g.,.2%). The latter approach would significantly improve payment testing accuracy without reducing the protein level. If this latter course were to be taken, we are sure that selection of the exact magnitude of the constant value to be added to all tests would be a matter of considerable debate. In spite of these issues, clearly the current system of using 1N as a basis for calibration of infrared milk analyzers in the US introduces very important systematic errors in protein tests that cause economic inequity among producers. Systematic errors in milk protein data used for genetic selection and feeding management will impede progress in improvement of the protein content of milk. The economic impact of these errors will increase as the protein fraction of milk increases in value. The industry has been provided with reference methodology for TPN testing (6). Adoption of TP as the reference for calibration of infrared milk analyzers will allow these significant testing errors to be eliminated with little, if any, change in milk testing cost. Journal of Dairy Science Vol. 75, No. 11, 1992

8 BORDEN SYMPOSIUM 3217 REFERENCES 1 American Heart Association Dietary guidelines for healthy American adults. Am. Heart Assoc. Nat!. Clr., Dallas, TX. 2 Barbano, D. M Seasonal and regional variation in milk composition in the US. Page 96 in Proc Cornell Nutr. Conf. Feed Manuf., Rochester, NY. 3 Barbano, D. M., and J. L. Clark Infrared milk analysis-ehal1enges for the future. 1. Dairy Sci. 72: Barbano, D. M., J. L. Clark, C. E. Dunham, and 1. R. Heming Kjeldah1 method for detennination of total nitrogen content of milk: collaborative study. 1. Assoc. Offic. Anal. Chem. 73: Barbano, D. M., and M. E. DellaValle Seasonal variation in milk solids components in various regions of the U.S. 1. Dairy Sci. 68(Suppl. 1):71.(Abslr.) 6 Barbano, D. M., J. M. Lynch, and J. R. Heming Direct and indirect determination of true protein content of milk by Kjeldahl analysis: collaborative study. J. Assoc. Offic. Anal. Chem. 74: Bruhn, J. C., and A. A. Franke Regional differences in nitrogen fractions in California herd milks. J. Dairy Sci. 62: Franke, A. A., J. C. Bruhn, and C. M. Lawrence Distribution of protein in California milk in J. Dairy Sci. 71: Lynch, J. M., D. M. Barbano, and J. R. Heming Variation in the ash and nonprotein nitrogen content of milk, and use of milk protein content to predict milk ash content. 1. Dairy Sci. 73(Suppl. 1): 92.(Abstr.) 10 Milk Industry Foundation Milk Facts. Milk Ind. Found., Washington, DC. 11 Roseler, D. K., 1. D. Ferguson, and C. 1. Sniffen The effects of dietary protein degradabilityl undegradability on milk urea, NPN and blood urea in lactating dairy cows. J. Dairy Sci. 73(Suppl. 1): 168.(Abstr.) 12 Rowland, S The determination of the nitrogen distribution in milk. 1. Dairy Res. 9: Szijarto, L., D. A. Biggs, and D. M. Irvine Variability of casein, serum protein and nonprotein nitrogen in plant milk supplies in Ontario. 1. Dairy Sci. 56: Verdi, R. J., D. M. Barbano, M. E. DellaValle, and G. F. Senyk Variability in true protein, casein, nonprotein nitrogen, and proteolysis in high and low somatic cell milks. 1. Dairy Sci. 70: Walstra, P., and R. Jenness Page 137 in Dairy Chemistry and Physics. John Wiley & Sons, New York, NY. 16 Webb, B. H., A. H. Johnson, and J. A. Alford Page 41 in Fundamentals of Dairy Chemistry. AVI Publ. Co., Westport, CT. Journal of Dairy Science Vol. 75, No. 11, 1992