NIR SPECTROSCOPY FOR PREDICTION OF AMINO ACIDS IN FEED INGREDIENTS

ABSTRACT

Dirk Hoehler1, J. Goodson', J. Fontaine2

, A. Jaeger2 and B. Schirmer2

1Degussa Corporation, Feed Additives 1701 Barrett Lakes Boulevard, Suite 340

Kennesaw, GA 30144 dirk.hoehler@degussa.com

www.aminoacidsandmore.com 2Degussa AG, Hanau, Germany

Knowing the accurate amino acid contents in feedstuffs is essential to produce precise and cost­effective feed. Near-infrared reflectance spectroscopy (NIRS) calibrations have been developed to enable accurate and fast predictions of essential amino acids, protein and moisture in the most important feed ingredients. NIRS calibrations for single feed ingredients are based on large numbers of samples from global origin. With NIRS, raw materials can be screened regarding quality, origin or suppliers, and feed can be formulated based on actual amino acid contents. Six international collaborative trials in a network with more than 60 participating laboratories demonstrated that the calibrations provide reliable and precise predictions of amino acids. NIRS may also be used to estimate available amino acid concentrations in feed ingredients in the future.

INTRODUCTION

The feed industry needs rapid, accurate and inexpensive means for predicting amino acid (AA) contents of feed ingredients for use in feed formulation and quality control programs. Traditional amino acid analysis involves hydrolysis of protein followed by ion exchange chromatography (IEC) or high-performance liquid chromatography (HPLC). These wet chemistry procedures are time consuming, costly and produce chemical wastes, which cause disposal problems.

Near-Infrared Reflectance Spectroscopy (NIRS) has been used for feedstuff analysis for more than 30 years (Ben-Gea and Norris, 1968). In the early years, NIRS was mainly applied to moisture or crude nutrients of feedstuffs, such as determination of protein and oil by Hymowitz et al. (1974); analysis of protein and moisture in cereal grains by Williams (1975); or analysis of forage by Norris et al. (1976). In 1978, Rubenthaler and Bruinsma first reported a successful NIRS calibration for an amino acid, lysine. Afterwards, some further work on NIRS prediction of amino acids in selected feed ingredients followed; such as prediction of four limiting amino acids in wheat and barley (Williams et al., 1984), methionine in peas (Williams et al., 1986), or amino acids in soybeans (Pazdemik et al., 1997). Additionally, NIRS has also been tested to predict digestible amino acid contents of feedstuffs (Van Kempen and Simmins, 1997; Van Kempen et al., 1997; Van Kempen and Bodin, 1998). Although these data are quite substantial, the number of samples was often limited, which sometimes lead to disadvantageous combining

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of different feedstuffs into one calibration. Additionally, calibration samples were sometimes used for validations, which is not appropriate.

In the current paper we will discuss the most recent developments on amino acid prediction via NIRS in feed ingredients, including management of a worldwide network of NIRS laboratories and future developments.

NIRS CALIBRATION EQUATIONS FOR AMINO ACIDS IN FEED INGREDIENTS

Calibration Development - Carried Out For Individual Feed Ingredients

Amino acid analyses of feedstuffs are needed for accurate and cost-effective diet formulation. Traditional amino acid analysis involves hydrolysis of protein followed by ion exchange chromatography (IEC) or high-performance liquid chromatography (HPLC). Degussa has generated the world's largest database of 15000 analyzed samples of 130 different feed ingredients.

Table 1. Number of samples included for AminoNIR® calibrations of amino acids in different feed ingredients.

Feed ingredients Barley Com Com gluten meal DDGS (com basis) Feather meal Fish meal Lupins Meat meal products Peas Poultry meal Rapeseed and canola meal Rice bran, de-oiled and polishings Sorghum Soybean meal and full-fat soybeans Sunflower meal Triticale and rye Wheat Wheat bran and middlings

No. of samples 233 502 184 130 342 307 105 468 110 229 248 181 205 522 107 273 281 178

Several of Degussa's amino acid analysis methods were adopted as 'AOAC Standard Method'. For more than 20 years, Degussa has been involved in the development of new official analytical methods (Llames and Fontaine, 1994). However, these wet chemistry procedures are time consuming, costly, produce chemical wastes and require skilled technical personnel.

Based on the wet chemistry technology experience, Fontaine et al. (2001 and 2002) developed NIRS prediction equations for all essential amino acids in 24 major feed ingredients based on

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extensive calibration and validation data. Most of the calibrations are focused on single feed ingredients and are based on a large number of samples collected worldwide (Table 1 ). Meat meal products include meat meal, meat meal tankage as well as meat and bone meal. The poultry meal calibration covers all poultry meals from low-ash pet food grade to high-ash poultry by- · product meal qualities. DDGS represents the most recently developed calibration equation. All calibration equations are routinely updated and expanded, and we are always on the lookout for outliers.

Equations for dry matter, crude protein, lysine, methionine, cystine, Met + Cys, threonine, tryptophan, arginine, isoleucine, leucine, valine, histidine, and phenylalanine are available for each feed ingredient. Sample preparation includes grinding to 0.5 mm particle size using a Retsch ultra-centrifugal mill. The spectra were scanned on a Foss Model 5000 with monochromator (1100-2500 nm), spinning sample module and reflectance detector. Winisi II­software was used for calibration development. The spectra of individual calibration sets were combined with the analyzed values from the wet chemistry reference method for the calculation of the equations.

Validation - AminoNIR® Predictions of Amino Acids are Reliable

Calibrations were validated with independent samples before they were approved for routine applications. A total of 125 additional samples of soybeans and soybean meals and 200 of meat meal products were analyzed by NIRS as well as by the classical method followed by a comparison with the NIR values. The complete validation data for dry matter, crude protein and all essential amino acids are shown in Table 2. Linear regression equations with slope and R2

(square of correlation coefficient) were calculated.

Table 2. AminoNIR® validation statistics for independent samples of soybean meal and full-fat soybeans (number of sameles: n=l25, T!:£: n=481 and meat meal eroducts (number of sameles: n=200, T!:_E: n=92)

So:ybean meal and full-fat so;ybeans Meat meal Eroducts*

NIRS NIRS Classical Classical

Mean Mean Mean Mean Variables {%} {%} SEP R2 SloEe {%} {%} SEP R2 SloEe Dry matter 90.0 90.1 0.382 0.95 0.93 95.1 95.0 0.375 0.92 0.88 Crude protein 43.7 43 .6 0.532 0.99 0.99 52.7 52.62 1.206 0.97 0.97 Methionine 0.58 0.57 0.028 0.84 0.90 0.70 0.70 0.046 0.91 0.92 Cystine 0.66 0.66 0.032 0.84 0.84 0.48 0.47 0,061 0.91 0.86 Met+Cys 1.24 1.23 0.050 0.88 0.90 1.17 1.16 0.073 0.95 0.91 Lysine 2.64 2.64 0.074 0.95 0.97 2.60 2.62 0.110 0.95 0.97 Threonine 1.69 1.68 0.043 0.96 0.99 1.67 1.66 0.063 0.97 0.95 Tryptophan 0.58 0.58 0.013 0.97 0.97 0.31 0.31 0.018 0.98 0.95 Arginine 3.21 3.22 0.084 0.96 0.98 3.53 3.51 0.109 0.90 0.93 Isoleucine 1.96 1.97 0.048 0.96 1.01 1.43 1.43 0.066 0.96 0.96 Leucine 3.31 3.31 0.063 0.98 0.98 3.16 3.16 0.107 0.98 0.96 Valine 2.07 2.07 0.058 0.95 0.99 2.23 2.23 0.092 0.97 0.96 SEP = standard error of prediction R 2 = fraction of sample variation explained by the regression equation Slope = slope of regression line between classical (x) and NIRS values (y) * Meat meal products: meat meal, meat and bone meal, meat meal tankage

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Individual data of methionine and lysine in soybean and meat meal products are presented in Figures 1 and 2, by plotting wet chemistry values versus NIR values. The dotted line illustrates the ideal line with an intercept of 0 and a slope of 1, where classical and NIRS values would be the same. The fit of NIRS to classical values for all amino acids in both feed ingredients is good. Most R2 values exceed 0.90, often 0.95. In the case of sulphur amino acids, methionine and cystine, the prediction is slightly less accurate, which reflects the lower precision of the wet chemistry reference method.

0.73

0.68

0.63

0.58

0.53

0.48

0.43

NIR-value (%)

■

Methionine

y = 0.05 + 0.90x R2 = 0.84

0.38 --~----.------.-~--.-----,-----------,

0.38 0.43 0.48 0.53 0.58 0.63 0.68 0.73

Lab-value (%)

3.30

3.10

2.90

2.70

2.50

2.30

2.10

NIR-value (%)

Lysine

y = 0.08 + 0.97x R2 = 0.95

1.90 4----------r---~--------, 1.90 2.10 2.30 2.50 2.70 2.90 3.10 3.30

Lab-value (%)

Figure 1. Validation of NIRS predictions for methionine and lysine in soybean meals and full­fat soybeans.

1_35

NIR-value (%)

1.20

1.05

0.90

0.75

0.60

0.45

Methionine

V = 0.06 + 0.92x R 2 = 0.91

0.30 +---~----.-------.----,--,------,----,

0.30 0.45 0.60 0. 75 0.90 1.05 1.20 1.35

Lab-value (%)

3.90

3.40

2.90

2.40

1.90

NIR-value (%)

Lysine

R 2 = 0.95 1.40 4-----~--~-----,------,-----,

1.40 1.90 2.40 2.90 3.40 3.90

Lab-value (%)

Figure 2. Validation ofNIRS predictions for methionine and lysine in meat meal products.

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Worldwide Collaborative Trials to Check the Accuracy of NIRS

For quality assurance, Degussa organizes a world-wide collaborative trial most years to check agreement within the NIRS network. These trials have been conducted since 1999; the results of · the first 4 collaborative trials were published by Fontaine et al. in 2004. The most recent collaborative trial completed in 2005 included a total of more than 60 participating laboratories.

In the course of this process, we have improved our methods by switching to product-specific NIR-instrument standardization, which led to a marked reduction in the degree of variation, especially for feed ingredients with low amino acid contents, such as com (Fontaine et al., 2004). Continuous updating of calibrations by using repeatability files has further improved accuracy and precision within the NIRS network. The repeatability file contains spectra of the same samples measured on instruments within the global network under different operating conditions. This file can be calibrated into the respective raw material equation, thus reducing effects of different instruments and analytical conditions, such as temperature and atmospheric humidity.

Figure 3 illustrates the continuous improvement of the precision in the network documented with four worldwide collaborative trials. The CV for the major amino acids is in the order of 2-3%; the figures for dry matter and crude protein are well below that. Following the transfer of calibration equations, amino acids can be estimated worldwide in feed ingredients in excellent agreement with the master instrument and the reference method.

Coefficient of Variation(%)

10.0 □ Ring test 1: 5 Labs, 10 samples ll2I Ring test 2: 9 Labs, 15 samples ■ Ring test 3: 29 Labs, 20 samples ■ Ring test 4: 44 Labs, 16 samples

7 .5 ---- ----- --- ---- ---- ---- ---- --- -·-······· ··· ·· ·· ······· ·····- - -- ------- ---- ----- --- ·-·-· ··· ·· ·· ···· ···· ··· ···

5.0 ---- -----------·-·--- -- --- -- --- ------ ------------------

2.5

0 . 0 _,_,_........,__,_--"'

'0~ c,~ ~e'- ~~c, \,i~ ~~~ ~(~ i>-~~ ,,e '--e~ '1'3-\

Figure 3. NIRS collaborative trials - average variation in the world­wide network was continuously reduced by described measures (Fontaine et al., 2004).

NIRS - PRACTICAL USE FOR THE FEED PRODUCER

NIRS predictions are very fast, which enables integrators and feed producers to analyze more raw material samples for amino acids. With the obtained data, incoming raw materials can be screened more efficiently to optimize the feed formulation. The amino acid content in all feed

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ingredients varies as demonstrated in Figure 4. Apart from a medium quality of the raw material, there are always samples of low or top quality among different batches. The effect of varying amino acid contents on the daily feed production process becomes evident in the following example.

medium quality

% Amino acid

0.60 0.70 0.80 0.90 1.00 1.10 1.20

Figure 4. Theoretical distribution of different raw material batches.

Customer Case: Screening of Two Meat and Bone Meal Suppliers

A total of 20 meat and bone meals were sampled by a feed producer in the Midwestern USA across two different suppliers and analyzed for dry matter, crude protein and amino acids by NIRS. Depending on the suppliers, the crude protein and amino acid content varied substantially, which is shown in Figure 5.

56.25 3.1

2.9 54.25

'?!-c ~ 2.7

j 52.25 cli e C: 2.5 0. 'iii

> Q) 50.25 ..J

'O 2.3 ::::l

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Plant A Plant B Plant A Plant B

Figure 5. Crude protein and lysine contents of 20 meat and bone meals of two different suppliers, analyzed by NIRS.

The actual crude protein contents in all samples ranged from 48.35 to 55.86%. Meat and bone meals from supplier B showed a higher average value (54.91% vs. 50.31%) as well as a lower variation of the samples. A similar picture holds true for lysine as well. If feed formulation would be conducted based on mean or "table" values, the risk of under or over-formulation is considerable when using meat and bone meals from these two suppliers. Simple comparisons like this enable purchasers to calculate value differentiations between different suppliers or products based on actual protein or ( essential) amino acid costs.

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Figure 6 demonstrates how NIRS results can be utilized to generate profiles for competing suppliers. For the two suppliers of this particular ingredient the normal distribution curves are plotted separately. Besides the fact that the mean Met+ Cys value differs considerably, there is also a big difference in range and variability. If one picks a mean value minus one standard· deviation for the linear programming input, it is clear that the two suppliers of meat and bone meals will be valued very differently. At the same time, feed formulation can be expected to be more consistent with two different inputs being used.

% Met+Cys

Supplier 1

0.8 0.9 1.0 1.1 1.2 1.3 1.4 1.5

Figure 6. Comparison of Met+Cys values in meat and bone meals delivered by two different suppliers: mean and standard deviation (Supplier 1: 24 samples, supplier 2: 21 samples. Amino acids analyzed by NIRS).

AMINO ACID AVAILABILITY

The basic principle of efficient feed formulation is to determine the animal's (digestible) amino acid requirements and then supply (digestible) amino acids in their diet to meet their needs with minimal excesses.

Feed Formulation Based on Digestible Amino Acids

A good commercial feed manufacturing scheme should contain the following elements:

1. Classification of ingredients by source, process etc. 2. Total amino acid compositions covering at least 5 essential amino acids. 3. Continuous adjustment of amino acid values in the formulation matrix. 4. Adjustment for availability using digestibility coefficients.-5. Feed formulation control of (digestible) amino acid and protein levels. 6. Adjustment for digestible amino acids using a rapid method such as NIRS.

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While points 1 to 5 can be realized based on today's technology, the last point regarding adjustment for digestible amino acids based on NIRS has not yet been fully realized yet.

It is known that a proportion of dietary amino acids is excreted undigested and that individual raw materials differ widely in this respect. Thus, the higher the inclusion levels of raw materials with low amino acid digestibility in diets formulated on the basis of total amino acids, the less reliable will be the prediction of performance (for review, see Lemme et al., 2004). This applies especially for all heat processed feed ingredients, such as meat and bone meal, poultry meal, feather meal, soybean meal, canola meal, DDGS, etc. In general, costly safety margins are usually applied to avoid potential reductions in performance.

Knowledge of digestibility coefficients for individual amino acids in raw materials and the requirement of digestible amino acids for a defined production target (maximizing growth, breast meat yield, profitability, minimizing feed conversion ratio etc.) enables formulation of diets closer to the requirements of the animals. Diets based on digestible amino acids may encourage the use of alternative protein sources, because such formulations will improve the precision of least cost diets and reduce nitrogen output. Finally, diets formulated on a digestible amino acid basis may also offer economic benefits.

The data in Figures 7 and 8 (Pack et al. , 2001) will be used to illustrate these points. Amino acid contents and ileal digestibilities were investigated in 25 meat and bone meals and in 27 soybean meals. The meat and bone meals were collected in different European countries, while the soybean meals originated from several countries in North and South America. Both studies used the slaughter technique with growing broiler chickens from 14 to 24 days of age (Lemme et al. , 2004). Apparent ileal digestibility in the soybean meals did not vary substantially despite their origin (mean ±SD): Lys 90.0 ±1.6%, Met 90.6 ±1.5%, Cys 81.8 ±2.3%, Thr 83.6 ±1.7%. In contrast, in meat and bone meals there was a large variation of apparent ileal digestibilities (mean ±SD): Lys 67.7 ±10.9%, Met 71.4 ±9.9%, Cys 19.6 ±12.8%, Thr 59.2 ±8.2%.

Amino acid digestibility in meat and bone meals was generally low, Cys was notably poorly utilized. In both ingredients, there was virtually no correlation between the content of an amino acid in the sample and its digestibility coefficient. This data demonstrates that the quality of soybean meals as judged from ileal amino acid digestibility in broilers was consistently good, while meat and bone meals were extremely inconsistent. The latter calls for further work using rapid predictive tools, such as NIRS. The data also show the importance of sampling and analyzing "the right" ingredients, i.e., to concentrate on the most variable (heat-processed) feed ingredients within the production chain, possibly combined with real-time feed formulation matrix updates.

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Figure 7. Lysine content (black) and apparent ileal lysine digestibility (grey) in 27 soybean meal samples in growing broiler chickens (Pack et al., 2001 ).

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Figure 8. Lysine content (black) and apparent ileal lysine digestibility (grey) in 25 meat and bone meals in growing broiler chickens (Pack et al., 2001).

Reactive Lysine Assay

Processed feed ingredients or feed ingredients stored for long periods of time are very likely to contain a certain amount of damaged amino acids. This may render a proportion of the amino acids nutritionally unavailable. This is particularly true for lysine, which possesses an £-amino group that can react with a wide range of compounds present in the diet to produce compounds that may be partially absorbed from the gut but have no nutritional value to the animal (Hurrell

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and Carpenter 1981 ). A proportion of the reacted lysine derivatives are acid labile and can revert back to lysine during the acid hydrolysis step of conventional AA analysis. This does not, however, occur in the digestive tract. Consequently, the lysine concentrations of the feedstuffs and ileal digesta, determined by conventional AA analysis, will be misleading and the conventional true ileal digestibility assay will generally overestimate lysine availability in heat­treated feedstuffs (Moughan and Rutherfurd, 1996).

For unprocessed feedstuffs, the digestible reactive lysine content should be equivalent to the digestible lysine content determined using conventional methods, whereas for a processed feedstuffs, the total lysine content may be higher than the reactive lysine content due to the conversion of lysine derivatives to lysine during the acid hydrolysis stage of conventional AA analysis and total lysine digestibility will be lower. Overall, for the processed feedstuffs, the digestible available lysine content will be overestimated using conventional procedures. In severely damaged protein sources, some of the structurally altered lysine derivatives may be acid stable and thus may not convert back to lysine during acid hydrolysis. In this case, reactive and total lysine values should be more similar. The bioassay has been applied to a range of processed feedstuffs (Rutherfurd et al. 1997).

Reactive lysine content of heated field peas determined using the guanidation method and total lysine content dete1mined using conventional amino acid analysis (Rutherfurd and Moughan, 1997) are shown in Table 3. For the heated field peas, the reactive lysine content decreased from 98 % (unheated) to 61 %, respectively.

Table 3. Reactive lysine content (g/kg and relative, % ) of heated field peas dete1mined using the guanidation method and total lysine content (g/kg) determined using conventional amino acid analysis (Rutherfurd and Moughan, 1997).

Heating temperature (°C) Unheated 110 135 150 165

Total lysine 1.51 (100%) 1.53 (100%) 1.40 (100%) 1.21 (100%) 0.87 (100%)

Peas Reactive lysine

1.49(98%) 1.48 (97%) 1.30 (93%) 1.06 (88%) 0.53 (61 %)

Reactive lysine content of soybean meal and DDGS samples determined using the guanidation method and total lysine content determined using conventional amino acid analysis are shown in Tables 4 and 5. For the soybean meal samples, the reactive lysine content ranged from 80 to 95% of the total lysine value. The reactive lysine content in DDGS samples ranged from 66 to 84% of the total lysine value, with the two dark brown DDGS samples having the lowest reactive lysine content (66 and 68%) relative to the total lysine content.

These results indicate that a considerable portion of the total lysine analyzed in e.g. heated peas, soybean meal and DDGS using conventional amino acid analysis is determined in a form that can only be inefficiently utilized by the animal. As such, the conventional amino acid analysis overestimates lysine availability in heat-damaged feedstuffs.

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Table 4. Reactive lysine content (g/kg and relative, % ) of soybean meal samples determined using the guanidation method and total lysine content (g/kg) determined using conventional amino acid analysis (Degussa, 2005).

Sample no. 1 2 3 4 5 6

Total lysine 2.292 (100%) 2.670 (100%) 2.837 (100%) 3.060 (100%) 3.470 (100%) 3.999 (100%)

Soybean meal Reactive lysine

1.837 (80%) 2.347 (88%) 2.651 (93%) 2.892 (95%) 3.125 (90%) 3.761 (94%)

Table 5. Reactive lysine content (g/kg and relative, % ) of DDGS samples determined using the guanidation method and total lysine content (g/kg) determined using conventional amino acid analysis (Degussa, 2005).

Sample no. and color 1, dark brown 2, dark brown 3, yellow 4, yellow 5, yellow 6, yellow

Total lysine 0.560 (100%) 0.570 (100%) 0.780 (100%) 0.720 (100%) 0.800 (100%) 0.900 (100%)

DOGS Reactive lysine

0.372 (66%) 0.387 (68%) 0.655 (84%) 0.567 (79%) 0.648 (81 %) 0.733 (81 %)

For the four major amino acids, lysine, methionine, threonine and tryptophan and for phenylalanine, which are metabolized in the liver, heat damage during processing apparently causes changes to occur which have little effect on ileal digestibility, but result in a considerable portion of these amino acids apparently being absorbed in a form that is inefficiently utilized. On the other hand, the branched-chain amino acids, valine, isoleucine and leucine all appear to be unaffected by processing conditions and the ileal digestibility assay appears to reflect availability.

The results above look very clear and promising. Thus, the Reactive Lysine Assay may provide the basis for NIRS calibration developments for predicting available lysine/amino acid concentrations in feed ingredients.

CONCLUSIONS

Near-infrared reflectance spectroscopy has proven to be an.excellent and accurate tool for amino acid prediction in feed ingredients. However, the technology is currently not utilized to its full potential by the feed manufacturing industry. Future developments include routine screenings of variable feed ingredients combined with real-time matrix updates as well as fast prediction of available amino acid concentrations.

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LITERATURE CITED

Ben-Gea, I. and Norris, K.H. 1968. Determination of moisture content in soybeans by direct spectrophotometry. Israel J. Agric. Res. 18:125-135.

Fontaine, J., Hoerr, J., and Schirmer, B. 2001. Near-infrared reflectance spectroscopy enables the fast and accurate prediction of the essential amino acid contents in soy, rapeseed meal, sunflower meal, peas, fishmeal, meat meal products, and poultry meal. J. Agric. Food Chem. 49:57-66.

Fontaine, J., Hoerr, J., and Schirmer, B. 2002. Near-infrared reflectance spectroscopy (NIRS) enables the fast and accurate prediction of essential amino acid contents. 2. Results for wheat, barley, com, triticale, wheat bran/middlings, rice bran, and sorghum. J. Agric. Food Chem. 50:3902-3911.

Fontaine, J., Hoerr, J., and Schirmer, B. 2004. Amino acid contents in raw materials can be precisely analyzed in a global network of near-infrared spectrometers: Collaborative trials prove the positive effects of instrument standardization and repeatability files. J. Agric. Food Chem. 52:701-708.

Hurrell, R.F. and Carpenter, K.J. 1981. The estimation of available lysine in foodstuffs after Maillard reactions. Prog. FoodNutr. Sci. 5:159-176.

Hymowitz, T., Dudley, J.W., Collins, F.I., and Brown, C.M. 1974. Estimations of protein and oil concentration in com, soybean and oat seed by near infrared light reflectance. Crop Sci. 14: 713-715.

Lemme, A., Ravindran, V. , and Bryden, W.L. 2004. Ileal digestibility of amino acids in feed ingredients for broilers. World's Poultry Sci. J. 60:423-437.

Llames, C.R. and Fontaine, J. 1994. Determination of amino acids in feeds: Collaborative study. J. AOAC Int. 77 :1362-1402.

Moughan, P.J. and Rutherfurd, S.M. 1996. A new method for determining digestible reactive lysine in foods. J. Agri. Food Chem. 44:2202-2209.

Norris, K.H. , Barnes, R.F. , Moore, T.E. , and Shenck, T.S. 1976. Predicting forage quality by infrared reflectance spectroscopy. J. Anim. Sci. 43:889-897.

Pack, M. , Roehler, D., Rostagno, H.S. , Cremers, S., and Pallauf, J. 2001. Ileal digestibility of amino acids from soybean or meat & bone meals in broilers. Poultry Sci. 80: 1011-1012.

Pazdernik, D.L. , Killam, A.S ., and Orf, J. H. 1997. Analysis of amino and fatty acid composition in soybean seed using NIR-spectroscopy. Agron. J. 89:679-685 .

Rubenthaler, G.L. and Bruinsma, B.L. 1978. Lysine estimation in cereals by NIR. Crop Sci. 18: 1039-1042.

Rutherfurd, S.M. and Moughan, P .J. 1997. Application of a new method for detennining digestible reactive lysine to vaiably heated protein sources. J. Agri. Food Chem. 45:1582-1586.

Rutherfurd, S.M., Moughan, P.J. , and van Osch, L. 1997. Digestible reactive lysine in processed feedstuffs : application of a new bioassay. J. Agri. Food Chem. 45 :1189-1194.

Van Kempen, T. and Simmins, P.H. 1997. NIRS in precision feed formulation. J. Appl. Poultry Res. 6:471-477.

Van Kempen, T. , Williams, P., and Jackson, D. 1997. NIRS as a tool to predict hue ileal digestible amino acid contents offeedstuffs. Proc. Contr. Qual. 9: 123-126.

Van Kempen, T. and Bodin, J.-C. 1998. NIRS appears to be superior to nitrogen-based regression as a rapid tool in predicting the poultry digestible amino acid content of commonly used feedstuffs . Anim. Feed Sci. Technol. 76: 139-147.

Williams, P.C. 1975. Application of near-infrared spectroscopy to analysis of cereal grains and oilseeds. Cereal Chem. 52 :561-576.

Williams, P.C. , Mackenzie, S.L. , and Starkey, P.M. 1985. Determination of methionine in peas by NIRS. J. Agric. Food Chem. 33:811-815.

Williams, P.C. , Preston, K.R., Norris, K.H., and Starkey, P.M. 1984. Determination of amino acids in wheat and barley by near-infrared reflectance spectroscopy. J. Food Sci. 49:17-20.

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