AJINOMOTO EUROLYSINE S.A.S. Formulator s Handbook. Measuring and Predicting Amino Acid Contents in Feedingstuffs GO TO ESSENTIALS.

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1 GO TO ESSENTIALS Information N 32 July 2014 New Edition AJINOMOTO EUROLYSINE S.A.S. Formulator s Handbook Measuring and Predicting Amino Acid Contents in Feedingstuffs Chromatogramme Tables included Amino acid contents of 44 feedingstuffs. Intra-ingredient variability... First and final flaps Amino acid profile of 44 feedingstuffs. Inter-ingredient variability... Pages Native free amino acid contents of 44 feedingstuffs... Pages Equations of prediction of the amino acid contents of 12 feedingstuffs... Pages 32-35

2 hreonine % Methionine % Cystine % Tryptophan % Median Mean Std CV Min Max Median Mean Std CV Min Max Median Mean Std CV Min Max Median Mean Std CV Preface % % % % % % % % % % % % % % % % % % % % It 0.04 is the 14% formulator s 0.15 task 0.22(and 0.17 headache) to produce 18% % % feed recipes with given nutritional values, despite % % % % the raw materials to be mixed having different and variable characteristics. This guide is designed for all the 0.02 nutritionists 3% (in 0.19 the feed 0.23industry 0.22 as 0.22 well as 0.01 in research 4% % % and 0.05education) 3% who 1.14formulate 1.27 diets 1.22 and/or 1.21 are 0.04 involved 3% in % % ingredient % and feed 0.21evaluations The 0.23 objective 0.02is, based 8% % % on our own experience in amino acids, to propose and % % % % compare several practical tools to determine the nutritional value of ingredients, with a view of better controlling the % nutritional value of the resulting compound feeds. 4% % % The 0.03first 3% and only 0.45direct 0.54way 0.49 to assess amino 6% acid % % 2.13 contents in feedstuffs 1.39 is 1.99 to analyze 1.74 samples using a specific method. Amino acid contents can be measured with good precision using a reference method in a renowned laboratory. The repeatability and reproducibility levels are at least similar to those of proximal analyses. This guide advises how to choose the analytical method and the laboratory, and how to interpret analytical reports. In case analyses cannot be performed, the formulator may need indirect evaluations of amino acid contents. AJINOMOTO EUROLYSINE S.A.S. Customer Laboratory routinely analyzes ingredients, and therefore compiles a database. This database was computed in order to supply information on the variations in amino acid contents of ingredients for predictive purpose. In this new edition (July 2014), the dataset contains 44 ingredients commonly used in animal nutrition in Europe. It is Tyrosine % based on the systematic analysis Histidine of % all 20 amino acids (no missing Serine values) % in more than 950 samples. A table Alanine % Median Mean the Std first and CV final flaps Min reports Max Median the minimum, Mean Std maximum, CV median Min and Maxmean Median amino Mean acid Std contents, CV as well Minas the Max standard Median Mean deviation and the number of samples analyzed. The variability explained by the experimental procedure was minimized due Std CV to 0.05 the application 16% 0.19 of a strict 0.31 analytical procedure 0.03 (same 11% reference 0.37 method in one 0.50 unique 0.06 laboratory), 11% so 0.32 that the 0.49remaining % variability % could be 0.16 mostly 0.30 assigned 0.22 to 0.22 the feedstuff % origins. Intra and 0.57inter-feedstuffs variability % should 0.33 be interpreted with % care 0.03because 10% sampling was not 0.21 meant 0.22 to study 0.02 the 8% origin of 0.31 variability 0.45 (they 0.36were 0.37 received 0.03 for 8% quality control 0.47 from 0.72 external % requests) % In general, 0.22 any 0.27 table 0.24 should 0.24 be regarded % as informative and not 0.48 explanatory % % % % % % Crude protein, as defined by the nitrogen content multiplied by the conventional conversion factor 6.25, is generally used % % % % to represent the total amino acid content of feedstuffs and mixtures. Based on our feedstuffs data, we have demonstrated % % % % using linear regression that the estimation is generally biased and that the bias depends on feedstuffs categories, which is a problem for feed formulation. Using crude protein as a nutritional criterion is therefore not recommended % % % % Nitrogen % content 1.50 itself 1.63 is of little 1.56nutritional relevance 3% (what 3.48 is the 3.92 meaning 3.69 and 3.71 the level 0.12 of 3% the requirement in nitrogen % of an animal?), but tends to be a good predictor of individual amino acid contents in many feedstuff categories. Linear % % % % regressions between nitrogen (as independent variable) and individual amino acids (as dependent variables) have been % % % % established for every amino acid and the 23 ingredients with sufficient number of observations. The quality of the statistical models was checked using not only the coefficient of determination R², but also the distribution of residuals and the confidence % interval 1.11 of the 1.24 coefficients Among 0.04 the 23 3% feedstuffs, showed 3.11clear 3.09 regressions, % which are 4.98presented in the % 0.58 feedstuffs monographs pages The monographs provide 0.61 not 1.23only the slope 0.92 and intercept of each 0.84 regression, 1.60 but also 0.02an upper 2% bound 0.60 of 0.71 the prediction limits Finally, 5% the accuracy of the 1.22 prediction 1.22 of 0.03 amino acid 2% contents 1.77 based 1.97 on 1.84the % 3.09 regressions is compared with the accuracy 1.35 of the estimation 2.38 based 3.28 on the mean 2.89 table values In summary, amino acid contents in a formulation matrix can be estimated, inter alia, by direct analysis, by table values, or by analysis of nitrogen and application of linear regressions. The present document provides for these three methods an estimation of the error of prediction, so that the reader is able to estimate the cost:benefit ratio of updating his formulation matrix or not, through one or another method. Statistics provide guidance, but the decision will always be the responsibility of the expert. We do hope that not only the data, but also the evaluation of the dataset, based on our sensibilities, may help formulators in their mission. Étienne CORRENT, Éric LE GALL, Aude SIMONGIOVANNI (Nutritionists) and Marcelle EUDAIMON (Analyst)

3 Valine % Isoleucine % Leucine % Arginine % Min Max Median Mean Std CV Min Max Median Mean Std CV Min Max Median Mean Std CV Min Max Median Mean Std CV % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % Aspartic acid * % Glutamic acid * % Glycine % Proline % Min Max Median Mean Std CV Min Max Median Mean Std CV Min Max Median Mean Std CV Min Max Median Mean Std CV % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % Source: AJINOMOTO EUROLYSINE S.A.S. laboratory analysis database, as of January All analyses have been performed in AJINOMOTO EUROLYSINE S.A.S. Customer Laboratory (Amiens, France) following Repealed standard AFNOR XP V for tryptophan and Commission Regulation N 152/ EN ISO for all other amino acids. * Due to the analytical procedure, aspartic acid (Asp) and glutamic acid (Glu) represent the total amounts, aspartic acid (Asp) plus asparagine (Asn), and glutamic acid (Glu) plus glutamine (Gln), respectively.

4 Lysine % Threonine % Methionine % Cystine % Tryptophan % n Min Max Median Mean Std CV Min Max Median Mean Std CV Min Max Median Mean Std CV Min Max Median Mean Std CV Min Max Median Mean Std CV Cereals Cereals by-products Wheat % % % % % Barley % % % % % Corn % % % % % Triticale % % % % % Oats % % % % % Rice % % % % % Rye % % % % % Sorghum Wheat middlings & bran % % % % % Wheat gluten % % % % % Wheat gluten feed % % % % % Wheat DDGS % % % % % Corn feed flour Corn gluten meal 60 % % % % % % Corn germ Corn DDGS % % % % % Rice protein Phenylalanine % Tyrosine % Histidine % Serine % Alanine % n Min Max Median Mean Std CV Min Max Median Mean Std CV Min Max Median Mean Std CV Min Max Median Mean Std CV Min Max Median Mean Std CV Cereals Cereals by-products Wheat % % % % % Barley % % % % % Corn % % % % % Triticale % % % % % Oats % % % % % Rice % % % % % Rye % % % % % Sorghum Wheat middlings & bran % % % % % Wheat gluten % % % % % Wheat gluten feed % % % % % Wheat DDGS % % % % % Corn feed flour Corn gluten meal 60 % % % % % % Corn germ Corn DDGS % % % % % Rice protein Table 4 Part 1: Amino acid contents of Cereals and Cereals by-products (% as fed basis). For Vegetable protein sources, Dairy products and Miscellaneous, report to Table 4 Part 2 (final flap).

5 Valine % Isoleucine % Leucine % Arginine % Min Max Median Mean Std CV Min Max Median Mean Std CV Min Max Median Mean Std CV Min Max Median Mean Std CV % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % Aspartic acid * % Glutamic acid * % Glycine % Proline % Min Max Median Mean Std CV Min Max Median Mean Std CV Min Max Median Mean Std CV Min Max Median Mean Std CV % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % Source: AJINOMOTO EUROLYSINE S.A.S. laboratory analysis database, as of January All analyses have been performed in AJINOMOTO EUROLYSINE S.A.S. Customer Laboratory (Amiens, France) following Repealed standard AFNOR XP V for tryptophan and Commission Regulation N 152/ EN ISO for all other amino acids. * Due to the analytical procedure, aspartic acid (Asp) and glutamic acid (Glu) represent the total amounts, aspartic acid (Asp) plus asparagine (Asn), and glutamic acid (Glu) plus glutamine (Gln), respectively.

6 Table of contents Introduction Measuring Amino Acid Contents in Feedstuffs Methods How to Select the Adequate Analysis? The Analysis of Total Amino Acids The Analysis of Free Amino Acids Accuracy of the Methods and Results Definitions Repeatability and Reproducibility of Amino Acid Analyses How to Maximize Results Accuracy? Interpretation of Analytical Results How to Read an Analytical Report? How to Use Analytical Results to Make Decisions? Predicting Amino Acid Contents in Feedstuffs AJINOMOTO EUROLYSINE S.A.S. Feedstuffs Database Univariate Models Lead to Table Values Total Amino Acid Contents in 44 Feedstuffs Mean Amino Acid Profiles Native Free Amino Acid Contents Bivariate Models Lead to Predictive Equations Correlations Between Amino Acid Contents Linear Regressions with Nitrogen as Independent Variable Risk Management in Feedstuffs Evaluation Precision of Predictions Based on Table Values Precision of Predictions Based on Linear Regressions Compared Precision of Table Values, Predictive Equations and Analyses Conclusion: Which Amino Acid Values for the Formulation Matrix? Reference List The 20 proteinogenic amino acids Information n 32 AJINOMOTO EUROLYSINE s.a.s.

7 Introduction Monitoring amino acid contents in feedstuffs is necessary to avoid under or overformulations Alpha-amino acids are important nutrients because they are the building blocks for proteins, which have structural or functional functions in the organism. They can also be converted into other types of molecules with metabolic activities: e.g. tryptophan is a precursor of serotonin, a neuromediator involved in appetite, mood and behavior regulations (Primot and Melchior, 2008). According to the factorial approach, amino acid requirement can be estimated by the sum of the levels needed for the different functions (growth, renewal of tissues, immunity ). It can thus vary in function of the physiological stage of the animal, health status, etc. In case dietary supply is below requirement, animal growth performance will be reduced. Having a good knowledge of the amino acid contents in feedstuffs is key to adapting supplementation and ensuring that the feed mix target values will be met. An over-estimation of feedstuffs values would result in lower amino acid contents in the mix with subsequent lower performance. An under-estimation of feedstuffs values would result in expensive wasting of amino acids. For those feedstuffs that are particularly variable (effect of botanic variety, fertilization, technological treatment, etc.) amino acid estimates used to run feed formulations should be updated as frequently as needed. The quantification of total amino acids is necessary to determine the digestible amino acid contents of feedstuffs Focus A nutritionnist s point of view For each amino acid, one of the best nutritional criteria should be the quantity that is really available to the animal s metabolism. This amount is approximated by the quantity absorbed at the end of the ileum. The method of quantification of standardized ileal digestible (SID) amino acids in feeds involves operated animals (with a T-cannula in the ileum or with ileo-rectal anastomosis). Obviously it cannot be used routinely for feedstuffs evaluation. However mean SID coefficients have been published for the major ingredients used in diets for farm animals. Therefore, SID amino acids are currently best estimated by the multiplication of mean digestibility coefficients from tables with estimated total amino acid contents. Amino acid contents should be expressed on a SID basis. Experimental data confirmed that pigs fed similar total lysine level but lower SID lysine level (calculated using the digestibility coefficients from two different tables) had lower body weight gain and nitrogen retention (Wesseling and Liebert, 2002). To our knowledge, there is no publication available that would suggest that, within a feedstuff category, digestibility coefficients may or may not vary with amino acid contents. In absence of data, digestibility coefficients and total amino acid contents are considered to be independent variables. It is assumed that the sole estimation of total amino acid contents improves the estimation of SID amino acid contents. Amino acid contents can be estimated using different methods (prediction based on table values, on regressions, on near infra-red spectrometry, etc.). What all these models have in common is to have been calibrated and/or validated using the dosage of amino acids. The first section of this document is therefore devoted to amino acid analyses. The intention is to provide to the non-specialists, basic information on the principles and accuracy of the method. The second section will compare two predictive tools (table values and regressions) which were derived from AJINOMOTO EUROLYSINE S.A.S. Feedstuffs Database. AJINOMOTO EUROLYSINE s.a.s. Information n 32 3

8 1. Measuring Amino Acid Contents in Feedstuffs 1.1. Methods How to Select the Adequate Analysis? Analytical method must be specific An analysis is designed to answer a question, and should therefore be adapted to the purpose of the request, in terms of specificity, limits of detection or determination, and accuracy. Nitrogen analysis is not specific of amino acids Current affairs should remind us that nitrogen analysis is not specific of proteins or amino acids and thus should not be used alone for quality control. Focus An analyst s point of view The melamine case: Significant quantities of melamine have been found in various feedand foodstuffs, even though it should not be naturally present. This compound contains 66% nitrogen by mass. Using nitrogen analysis only, it is impossible to determine if a food sample with high nitrogen content has high protein content or if it is contaminated with a non-protein source like melamine. But high protein foodstuffs would also have greater amounts of amino acids. The total amino acid to nitrogen ratio is quite constant within a foodstuffs category. Thus a significantly lower amino acid to nitrogen ratio in one sample would suggest the presence of a non-protein nitrogen source. The L-Tryptophan case: It happened that some feed-grade L-Tryptophan products entered the European market with a lower tryptophan content than authorized by the E.U. regulation, while the certificate of analysis showed a regular level. How can that be? The method applied for the quality control was European or US Pharmacopeia: the perchloric titration method is based on measurement of amino functions (-NH2) in acidic medium. Amino acids are not the only compounds that contain amino groups, thus the method is not specific to amino acids. Therefore it should not be used alone, but in conjunction with other analyses (measurement of impurities, specific rotation, etc.). Definitions of official, standardized and validated methods When they are available, official or standardized methods must be applied in priority. Official methods are published in Official Journals (e.g. the Directives of the European Commission); standardized methods are published by committees such as AFNOR 1 in France, CEN 2 in Europe, AOAC 3 and ISO 4 worldwide. In-house methods can also be applied if they have been validated and recognized to give the same results as the reference methods. For total tryptophan determination in feedstuffs, AJINOMOTO EUROLYSINE S.A.S. Customer Laboratory implements the abrogated French norm AFNOR XP V This method has been validated and has received accreditation by the certification organization COFRAC 5. Figure 1 gives a non exhaustive list of analytical methods available and recognized to be valid for the determination of total and free amino acids in feedstuffs. Their principles are reported in the next section of this document. 1) Association Française de Normalisation. 2) European Committee for Standardization. 3) Association of Analytical Communities. 4) International Organization for Standardization. 5) Comité français d accréditation. AJINOMOTO EUROLYSINE S.A.S. Customer Laboratory has received accreditation from this body for the determination of all total amino acids in feedstuffs, concentrates and complete feeds. 4 Information n 32 AJINOMOTO EUROLYSINE s.a.s.

9 Which type of sample is to be analyzed? Which amino acid will be determined? Official Methods Standard Methods Other validated Methods Feedstuffs Complete feeds Concentrates Total Tryptophan Free Tryptophan All other Total amino acids All other Free amino acids Commission regulation N 152/2009 Commission regulation N 152/2009* EN ISO EN ISO 13903* Adapted from abrogated standard AFNOR XP V18-114* Feed-use amino acids Premixes Free Lysine, Threonine and Methionine NF EN ISO 17180* AOAC * All other Free amino acids Adapted from NF EN ISO 17180* Adapted from AOAC * Methods marked with an * are implemented in AJINOMOTO EUROLYSINE S.A.S. Customer Laboratory. Source: AJINOMOTO EUROLYSINE S.A.S. Figure 1: Analytical methods available for the determination of amino acid contents in raw materials, feeds, concentrates, pure amino acids and premixes The Analysis of Total Amino Acids General Principle The method of determination of amino acid contents in feedingstuffs is standardized The method of determination of total amino acid 1 contents in raw materials and feeds is standardized (EN ISO 13903) and has also been published by the European Commission (Commission regulation N 152/2009). The method is specific to amino acids based on the fact that amino acids are cations at ph 2.2 and give a specific colored reaction with ninhydrin. It does not determine the dosage of methionine hydroxy-analogue because the compound is not an amino acid. Dosage of total amino acids is carried out after hydrolysis of the proteins under acidic conditions for 23 hours at 110 C. This hydrolysis makes it possible to obtain all the amino acids except methionine and cystine. Preliminary performic acid oxidation of the methionine (transformed into methionine sulfone) and of the cystine 2 (transformed into cysteic acid) makes it possible to obtain all the amino acids except tyrosine and phenylalanine, which must be determined using hydrolysis without oxidation. The hydrolysates are then adjusted to a ph of 2.2 to have the amino acids in cationic form for the resin exchange step. Amino acids are separated by ion exchange chromatography. Amino acid contents are determined, after reaction with ninhydrin, using photometric detection at 440 nm for proline and 570 nm for all other amino acids. Amino acid analyses at AJINOMOTO EUROLYSINE S.A.S. Customer Laboratory Focus An analyst s point of view For the determination of total amino acid contents in feedstuffs, AJINOMOTO EUROLYSINE S.A.S. Customer Laboratory uses the standard method EN ISO (Figure 2) with two options. In the general case, one hydrolysis without oxidation and one with oxidation are performed resulting in duplicate values for all amino acids, except Met, Cys, Phe and Tyr. In some cases, our experience indicates that it is preferable to perform an additional hydrolysis without oxidation which leads to duplicate values for all amino acids except Met and Cys. Moreover, we decided that new hydrolyses would be performed in case the first results would differ by more than 3% for several amino acids. 1) Other than tryptophan, see next chapter. 2) In this document, the term cystine refers to cystine + cysteine. AJINOMOTO EUROLYSINE s.a.s. Information n 32 5

10 TEST SAMPLE Grinding Oxidation (protection of Met & Cys) Hydrolysis (23h C - HCI 6N) Hydrolysis (23h C - HCI 6N) Different sand baths and days Chromatography (amino acid analysis) Separation on ions exchange resins column Colorimetric reaction with nynhydrin Calibration with amino acids reference solution prepared by the laboratory All amino acids except Met & Cys All amino acids except Phe & Tyr Evaluation of the results Validation and drift follow up with amino acids solution commercially available RESULTS Figure 2: Determination of total amino acids in feedstuffs: Protocol used in AJINOMOTO EUROLYSINE S.A.S. Customer Laboratory (standard method EN ISO 13903) The Specific Case of Tryptophan Tryptophan is analyzed according to a specific method Tryptophan is destroyed by acidic hydrolysis and therefore requires a specific method of analysis. The principles are described in the standard method EN ISO and in the corresponding official method published by the European Commission (Commission regulation N 152/2009). Determination of total tryptophan is carried out after basic hydrolysis of the proteins: The sample is hydrolyzed under alkaline conditions with barium hydroxide. Hydrolysates are acidified with chlorhydric acid at ph 3.0 to perform the separation of the peak by chromatography. The tryptophan from the hydrolysates is separated by reverse phase high performance liquid chromatography (HPLC) and does not need any colorimetric reaction. Indeed, tryptophan contains an indol functional group which gives spectroscopic properties of absorption and fluorescence in the UV spectrum. Tryptophan analysis at AJINOMOTO EUROLYSINE S.A.S. Customer Laboratory Focus An analyst s point of view In AJINOMOTO EUROLYSINE S.A.S. Customer Laboratory, total tryptophan is analyzed in feedstuffs using an in-house method (Figure 3) adapted from the abrogated standard AFNOR XP V This method follows the same principles as the EN ISO standard and has proved to give similar results. It has been validated and has received COFRAC accreditation. The advantages compared to the EN ISO method are reduced time of analysis and lower recourse to toxic chemical products (orthophosphoric acid, methanol, trichloromethylpropanol and ethanolamine). All samples are analyzed in duplicate with a reference sample in each serial in order to verify the good quality of the hydrolysis. Moreover, according to the standard, new hydrolyses are performed in case the spread between both results would be greater than to 2%. 6 Information n 32 AJINOMOTO EUROLYSINE s.a.s.

11 TEST SAMPLE Grinding Hydrolysis in autoclave (16h C - Baryum hydroxide) Hydrolysis validation with a reference sample Chromatography (tryptophan analysis) Reverse phase high performance liquid chromotography on column Nova Pack C 18 or ODS Opersil Fluorometric detection Calibration with reference solution prepared by the laboratory Drift follow up with pure Tryptophan Evaluation of the results RESULTS Figure 3: Determination of total tryptophan in feedstuffs: Protocol used in AJINOMOTO EUROLYSINE S.A.S. Customer Laboratory (validated method) The Analysis of Free Amino Acids If the goal of the analysis is to control the quality of feed manufacturing, samples can be analyzed for total and/or free amino acids: total amino acid contents should be compared to the expected levels in the formula; free amino acids should be compared to the fraction coming from the supplemented feed-use amino acids (see Chapter How to use analytical results to make decisions?) General principle Methods for the determination of free amino acid contents in feedingstuffs are also standardized Free amino acid contents are determined by Commission regulation N 152/2009 equivalent to the standard method EN ISO The principles are the same as for total amino acids: only the preparation of the sample changes with a simple extraction instead of a hydrolysis. The amino acids are extracted using chlorhydric acid. Co-extracted nitrogenous macromolecules are precipitated with sulfosalicylic acid and removed by centrifugation. The solution is filtered and adjusted to a ph of 2.2. The amino acids are separated on ions exchange resins and measured by photometric detection at 570 nm (440 nm for proline) after reaction with ninhydrin on a specific amino acid analyzer The Specific Case of Tryptophan The principles of the method are the same as for total tryptophan determination, with the exception of the preparation of the sample. Free tryptophan is extracted using chlorhydric acid. Tryptophan in the filtered solution is separated by reverse phase high performance liquid chromatography (HPLC) and determined by fluorometric detection. AJINOMOTO EUROLYSINE s.a.s. Information n 32 7

12 1.2. Accuracy of the Methods and Results Definitions Trueness and Precision A method is accurate if it is true and precise An analytical method should have a high level of accuracy. The concept of accuracy refers to two parameters defined by the standard ISO 5725: Trueness refers to the closeness of agreement between the arithmetic mean of a large number of test results and the true value (which is not known in the case of chemical assays) or the accepted reference value. Precision refers to the closeness of agreement between independent test results obtained under stipulated conditions (with repeatability and reproducibility data). The need to consider precision arises because tests performed on presumably identical raw materials in presumably identical circumstances do not give in general identical results. This is attributed to unavoidable random errors inherent in every measurement procedure: if the difference between two results can be attributed to the inherent variation of the measurement method, then the two results can be considered as similar. The measure of precision is usually expressed in terms of imprecision and computed as a standard deviation of the test results measured within a laboratory or undertaken by several laboratories. A lower precision is reflected by a larger standard deviation. Figure 4 represents, through a volley of arrows, methods that would be untrue and not precise, true but not precise, untrue but precise, and finally true and precise. An accurate method is a necessary condition to obtain accurate results. Independent measures True value (or reference value) Not True neither Precise! True but not Precise! Precise but not True! True and Precise! Figure 4: Accuracy of a method Analogy with a volley of arrows Source: courtesy of BIPEA Repeatability and reproducibility Precision includes repeatability and reproducibility Two conditions of precision have been found necessary: Repeatability concerns the quality of analyses carried out in one laboratory: tests are performed with the same method on identical test items using the same equipment within short intervals of time. Reproducibility is a twofold concept. Intra-laboratory reproducibility tests are performed with the same method on identical test items in the same laboratory with different operators using different equipments. Inter-laboratory reproducibility tests are also performed with the same method on identical test items, but in different laboratories with different operators and different equipments. 8 Information n 32 AJINOMOTO EUROLYSINE s.a.s.

13 Coefficients of variation of repeatability (CV r ) and of reproducibility (CV R ) are defined as the ratios between the standard deviations (s r and s R, respectively) and the average of the analyzed values. They are commonly used by laboratories to monitor the quality of analysis, independently of the type of samples analyzed. The precision limits of a method represent the acceptable difference between two results obtained on a unique sample. They are calculated as plus and minus a multiple of the standard deviations (s r or s R ) or of the coefficients of variation (CV r and CV R ). The factor used for inter-laboratory tests in the standards is 2.8 (1.96 * 2) for a probability level of 95%. The coefficient 2 is due to the fact that the analyses are carried out in duplicate in inter-laboratory tests Accuracy of analytical results Analytical results are accurate if both bias and uncertainty are low The accuracy of analytical results is described with two parameters, bias and uncertainty, corresponding to the concepts of trueness and precision, respectively: Bias represents the difference between the result and the true value or the reference value. From a practical point of view, the true value is not known. A laboratory can evaluate its bias compared to a reference value while participating in an inter-laboratory ring test. Uncertainty gives the statistical dispersion of the analyzed values. The standard deviation of repeatability (s r ) and standard deviation of reproducibility (s R ) are generally multiplied by a factor to obtain the limits of the confidence interval of the analysis. The coefficient is generally 1.96, for a probability level of 95% Repeatability and Reproducibility of Amino Acid Analyses Accuracy Data Published with the Standard Methods Accuracy of amino acid analyses is similar to the accuracy of proximal analyses The standards provide precision limits of the methods. They have been established based on ring tests in which participating laboratories had to comply with strict guidelines. The inter-laboratory reproducibility data takes into account the repeatability of each laboratory. For all amino acids, the coefficients of variation of repeatability and reproducibility are in the range 1-3% and 2-13%, respectively. These values obtained in the s are similar to the values provided for proximal analyses in their corresponding standards (Table 1). Analytes Lysine Method used Ring tests Year Participants CV r (%) CV R (%) Threonine EN ISO (2005) Methionine Cystine Tryptophan EN ISO (2005) before Fat NF V (1997) Crude fiber ISO 6865 (2002) Crude protein Kjeldahl: ISO (2005) Crude protein Dumas: NF V (1997) Crude protein Dumas: EN ISO (2008) Table 1: Compared accuracy of the standard methods for the determination of amino acids, fat, crude fiber and crude protein (source: standard methods listed in the table). AJINOMOTO EUROLYSINE s.a.s. Information n 32 9

14 Accuracy Data Obtained in Recent Ring Tests Recent ring tests show robust coefficients of variations for lysine, threonine and tryptophan below 5% The Bureau Inter Professionnel d'etude Analytique (BIPEA) is a non-profit making association based in France. It was created in 1970, on the initiative of the professional organizations of the fields of production, storage and transformation of cereals and of manufacture of feed for animal. They organize inter-laboratory comparison tests to define the laboratory abilities and bring them assistance to manage, maintain or improve their performances. Results of the ring tests are sent to the participants after a statistical study. Each laboratory can then evaluate its trueness by calculating the bias with the reference value calculated as the average of all the laboratories (except outliers) or calculated from a number of selected laboratories decided by the commission in charge of the test. Contrary to the standards where every laboratory analyses samples in duplicate, the ring test procedure here requires only one analytical result per laboratory. Reproducibility data therefore do not include within-laboratory variations. The robust standard deviation instead is defined in ISO giving the rules for the inter-laboratory tests to evaluate the competence of laboratories. The robust coefficients of variation of all amino acids are in the range 1-7% for sulfur amino acids and 1-5% for all others. These values are lower than those for the dosage of fat, and similar to those of the other proximal analyses (Table 2). Analytes Methods used Participants Robust CV (%) Lysine Threonine 1-3 EN ISO (2005) and other methods 9 Methionine 1-3 Cystine 3-7 Tryptophan EN ISO (2005) and other methods Ash European Directive CEE 71/250 and NF Fat ISO 6492 and NF V (1997) Crude fiber European Directive 92/89 and NF V Crude protein Kjeldahl: ISO (2005) or European Directive CEE 7299 or other methods Crude protein Dumas: NF V (1997) Table 2: Compared accuracy of the determination of amino acids, ash, fat, crude fiber and crude protein (source: BIPEA ring-tests performed in 2013 for amino acids and in 2007 and 2008 for other analytes, personal communication) Accuracy Data by AJINOMOTO EUROLYSINE S.A.S. In AJINOMOTO EUROLYSINE S.A.S., coefficients of variation for the analysis of lysine, threonine and tryptophan are always below 1.5% AJINOMOTO EUROLYSINE S.A.S. Customer Laboratory has set a procedure of control of the quality of their analyses: the same test samples are analyzed every month by different analysts on different equipments. The coefficient of variation calculated over several months represents the intra-laboratory reproducibility (Table 3). 10 Information n 32 AJINOMOTO EUROLYSINE s.a.s.

15 Analytes Reference Samples CV (%) Lysine Threonine NF EN ISO (2005) Soybean meal, pig and cat feeds Methionine Cystine Tryptophan In house accredited method Abroged AFNOR XP - V Casein, soybean meal, pig and cat feeds Crude protein Dumas: EN ISO (2008) Soybean meal, pasta, pig and cat feeds Table 3: Accuracy of the determination of amino acids and protein in AJINOMOTO EUROLYSINE S.A.S. Customer Laboratory (internal data obtained in 2013). The maximum value for the coefficient of variation is 1.5 % for all amino acids, including methionine and cystine even with the additional step of oxidation. Data interpretation: There s no point in being precise but untrue! Focus A nutritionnist s point of view The user may calculate expanded uncertainties with various factors (1, 2 or 3 x standard deviation) depending on his objective: characterization or quality control, of ingredients or of final feeds. The calculation of expanded uncertainties should be regarded as a tool designed to help the user to make decisions (acceptance or rejection of feedstuffs with regards to specifications, allocation of feeding values to ingredients, etc.). Example 1: Inspection Is the tolerance level in line with the accuracy of the methods? Where, on official inspection, the composition of a compound feedstuff is found to depart from the declared composition, the amended Council Directive 79/373/EEC of 2 April 1979 permits tolerances. For amino acids, where the content recorded is less than the declared content, the tolerance is 15 % of the declared content for lysine, threonine and methionine, and 20% for cystine and tryptophan. These tolerance levels correspond to 2 and 3 times the coefficients of variation recorded in recent ring tests 1 for methionine and cystine, respectively. For lysine, threonine and tryptophan, the tolerance level corresponds to at least 4 times normal coefficients of variation. It can be concluded that low values would be tolerated from an administrative standpoint, whereas they would be rejected from a nutritional standpoint. Example 2: Nutritional studies Should trial results be interpreted based on analyzed values rather than protocol values? When performing nutritional studies, it is highly recommended to control the amino acid contents in the experimental diets. In amino acid dose-responses, all diets should be checked well before the start of the experiment, so that the study manager has enough time to decide whether the diets are acceptable or should be re-manufactured. Diets will be accepted if the study manager considers that analyzed values are not significantly different from the theoretical values. In this case, there is no problem to use the protocol values for the interpretation of the results. Using analyzed values instead is more precise but would not be truer. 1) See the results of the ring tests monitored by BIPEA in 2013 (Chapter ). AJINOMOTO EUROLYSINE s.a.s. Information n 32 11

16 How to Maximize Results Accuracy? Analytical results are given with an uncertainty, which results from the method, but also from the analytical process (analyst, apparatus, traceability, etc.) and the feed material analyzed (sampling and handling). Selection of a skilled laboratory Importance of sampling The environment of the analysis should be optimal; the buildings and equipment should be adapted to the analytical activity. It is recommended to select an experienced laboratory (analysis of a large number of samples per year, expertise in the methodology). The laboratory should supervise metrological equipments, have preventive maintenance and checking of apparatus and have good traceability of data. Participation in ring tests allows laboratories to evaluate their performance and control the competence of the analysts. This list of criteria for the selection of a laboratory is indicative. Laboratories accredited according to the EN ISO standard meet all these requirements. It is the responsibility of the person requesting the analyses to ensure the good representativeness of the samples. A representative sample is defined as a small fraction from a batch in such a way that a determination of any particular characteristic of this fraction will represent the mean value of the characteristic of the batch. Sampling procedure depends on the nature of the materials to be analyzed. Recommendations in terms of procedure and equipment can be found in specific standards related to the general standard EN ISO Interpretation of Analytical Results How to Read an Analytical Report? Analyzed values are reported in a bulletin signed by the head of the laboratory. The document also indicates the method used. Figure 5, on page 13, illustrates how to read an analytical report, based on the current format provided by AJINOMOTO EUROLYSINE S.A.S. Customer Laboratory How to Use Analytical Results to Make Decisions? Calculation of the expanded uncertainty associated with an analytical result The expanded uncertainty, expressed as a multiple of the standard deviation, is a suitable quantitative indication of the quality of a result. In practice, a standard deviation of each sample cannot be given because samples cannot be analyzed ten times in routine. Tests are thus carried out regularly to check the uncertainty on samples of the same kind. Expanded uncertainty of any sample can then be calculated using the intra-laboratory reproducibility data. For analytical results obtained in the AJINOMOTO EUROLYSINE S.A.S. Customer Laboratory, we recommend the person to consider the interval Analyzed value (% as fed basis) +/- 3% for all amino acids except methionine and cystine where an interval Analyzed value (% as fed basis) +/- 6% should be considered due to the additional step of oxidation that may increase uncertainty. Practical example Focus A nutritionnist s point of view In Figure 5, total lysine is found to be 1.24% whereas the expected value was 1.30%. With an expanded uncertainty of 3%, the true lysine content may be in the range The applicant can suspect that lysine content is lower than expected. If all amino acid contents, as well as protein content, are lower than expected, the applicant should suspect that the results may be affected by sampling. It is then recommended to check the sampling procedure and to analyze another sample. 12 Information n 32 AJINOMOTO EUROLYSINE s.a.s.

17 ➊ ➊ COFRAC logo and foot notes indicate that analyses have been carried out under accreditation according to the ISO standard. ➋ For the sake of traceability, each sample is identified with the applicant s and the laboratory s references. ➋ Parameter Unit Method (1) Measure 1 Measure 2 Average Uncertainty Expected Value Dry Matter g%g 103 C 4h Crude protein TN x 6.25 g%g NF EN ISO * / Total Lysine g%g NF EN ISO 13903* / Total Threonine g%g NF EN ISO 13903* / Total Methionine g%g NF EN ISO 13903* / Total Cystine +Cystein g%g NF EN ISO 13903* / Total Methionine + Cystine g%g NF EN ISO 13903* Total Tryptophan g%g MOD.0094 version G* / ➌ ➍ ➎ ➏ ➐ ➑ Total Valine g%g NF EN ISO 13903* / Total Isoleucine g%g NF EN ISO 13903* / Total Leucine g%g NF EN ISO 13903* / Total Arginine g%g NF EN ISO 13903* / Total Phenylalanine g%g NF EN ISO 13903* / Total Tyrosine g%g NF EN ISO 13903* / Total Histidine g%g NF EN ISO 13903* / Total Serine g%g NF EN ISO 13903* / Total Alanine g%g NF EN ISO 13903* / Total Aspartic Acid g%g NF EN ISO 13903* / Total Glutamic Acid g%g NF EN ISO 13903* / Total Glycine g%g NF EN ISO 13903* / Total Proline g%g NF EN ISO 13903* / Free base Lysine g%g NF EN ISO 13903* Free Threonine g%g NF EN ISO 13903* Free Tryptophan g%g g MOD Free Valine g%g NF EN ISO 13903* ➊ ➌ The parameters analyzed are dry matter, crude protein (nitrogen * 6.25), total amino acids and free amino acids. The user can ask for the analysis of one, several, or all 20 amino acids. Note 1: Due to the analytical process, glutamine and asparagine are included in the glutamic acid and aspartic acid fractions, respectively. Note 2: Cystine includes both cysteine and cystine, which is a dimer of cysteine molecules joined by a disulfide bond. ➍ Results are given in weight percentage, as fed basis. ➎ Methods performed. Detailed information including accuracy data is available on request. ➏ Measure 1 and Measure 2 correspond to the respective measurements of each amino acid (replicates). As explained in the Focus page 5, Met, Cys, Phe and Tyr can be measured only one time. ➐ Average value is given with a range due to uncertainty. In our example, crude protein is 16.7% +/- 0.5 so the range is 16.2% to 17.2%. We can conclude that the measured value meets the expected value 16.8%. ➑ Expected values are provided by the person who requested the analysis. Total amino acids correspond to the total supply from ingredients and from amino acid supplementation. Note 1: Free methionine from DL-methionine is included in the total methionine level but not hydroxy analogue of methionine. If a feed is supplemented with hydroxy analogue of methionine, analyzed methionine level will be lower than the nutrient value of the formula. Note 2: Free lysine is expressed as base lysine. L-Lysine HCl contains minimum 97.5% lysine HCl but only 78% base lysine. Therefore, expected free lysine content in a diet with 6.3 kg/t L-Lysine HCl is 0.49% (= 78% * 0.63%). Figure 5: Contents of an analytical report by AJINOMOTO EUROLYSINE S.A.S. Customer Laboratory If only lysine content is lower than expected, the sample should be analyzed for supplemental free lysine. In this example, free lysine is found to be 0.44% whereas L-Lysine HCl was expected to be supplemented at a level of 0.63% (with a free lysine content of 78%, the expected lysine supply should have been 78% * 0.63% = 0.49%). These results suggest that the lower total lysine content may be due to a lower supplementation rate. If free lysine content would have been as expected, the user could have suspected an overestimation of the lysine content in one or several ingredients in the formulation matrix. AJINOMOTO EUROLYSINE s.a.s. Information n 32 13

18 AA, % Mean amino acid profile = n Mean Lys Thr Met Cys Trp Cereals Cereals by-products Vegetable protein sources Dairy products Miscellaneous Wheat Barley Corn Triticale Oats Rice Rye Sorghum Wheat middlings & bran Wheat gluten Wheat gluten feed Wheat DDGS Corn feed flour Corn gluten meal 60 % Corn germ Corn DDGS Rice protein Soybean meal Full fat soybean Soya protein concentrate % Soya protein concentrate 65 % Rape seed meal Full fat rape seed Sunflower meal 28 % Sunflower meal 33 % Sunflower meal 37 % Palm kernel meal Faba bean Lupin seed Pea Potato protein concentrate Milk Whey powder Whey protein concentrate Yoghurt Fish meal Blood meal Feather meal Poultry protein Plasma Egg Cassava Brewers' yeast Bakery by-products Table 5: Amino acid profile of 44 ingredients (each amino acid in % of the sum of all). 14 Information n 32 AJINOMOTO EUROLYSINE s.a.s.

19 Individual amino acids, % of the sum of all 20* amino acids ( AA) Val Ile Leu Arg Phe Tyr His Ser Ala Asp* Glu* Gly Pro Source: AJINOMOTO EUROLYSINE S.A.S. laboratory analysis database, as of January All analyses have been performed in AJINOMOTO EUROLYSINE S.A.S. Customer Laboratory (Amiens, France) following Repealed standard AFNOR XP V for tryptophan and Commission Regulation N 152/ EN ISO for all other amino acids. * Due to the analytical procedure, aspartic acid (Asp) and glutamic acid (Glu) represent the total amounts, aspartic acid (Asp) plus asparagine (Asn), and glutamic acid (Glu) plus glutamine (Gln), respectively. AJINOMOTO EUROLYSINE s.a.s. Information n 32 15

20 Native Free Lys % Native Free Thr % n Mean Std % tot Mean Std % tot Cereals Cereals by-products Vegetable protein sources Dairy products Miscellaneous Wheat % % Barley % % Corn % % Triticale % % Oats % % Rice % % Rye % % Sorghum % % Wheat middlings & bran % % Wheat gluten % % Wheat gluten feed % % Wheat DDGS % % Corn feed flour % % Corn gluten meal 60 % % % Corn germ % % Corn DDGS % % Rice protein % % Soybean meal % % Full fat soybean % % Soya protein concentrate % % % Soya protein concentrate 65 % % % Rape seed meal % % Full fat rape seed % % Sunflower meal 28 % % % Sunflower meal 33 % % % Sunflower meal 37 % % % Palm kernel meal % % Faba bean % % Lupin seed % % Pea % % Potato protein concentrate % % Milk % % Whey powder % % Whey protein concentrate % % Yoghurt % % Fish meal % % Blood meal % % Feather meal % % Poultry protein % % Plasma % % Egg % % Cassava % % Brewers' yeast % % Bakery by-products % % Table 6: Native free amino acid contents of 44 ingredients (% as fed basis), and % of the corresponding total amino acid (% tot)*. 16 Information n 32 AJINOMOTO EUROLYSINE s.a.s.

21 Native Free Met % Native Free Trp % Native Free Val % Native Free Arg % Mean Std % tot Mean Std % tot Mean Std % tot Mean % tot % tot % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % All analyses have been performed in AJINOMOTO EUROLYSINE S.A.S. Customer Laboratory (Amiens, France) following Repealed standard AFNOR XP V for tryptophan and Commission Regulation N 152/ EN ISO for all other amino acids. * Example: free Trp/Total Trp = 17% on average in wheat AJINOMOTO EUROLYSINE s.a.s. Information n 32 17

22 2. Predicting Amino Acid Contents in Feedstuffs The best estimation of amino acid contents is based on the analysis of a representative feedstuff sample. Amino acid analyses however are generally not performed routinely in the feed industry, because the method requires specific apparatus, dedicated analyst, and time (it takes at least a week to get results, while the ingredients might already have been mixed in the feed mill). In practice, formulators will have recourse to indirect estimations of the amino acid contents. One technique consists of the accumulation of analyses of batches of ingredients of the same type, statistical analysis of the dataset, elaboration of a model and utilization of the model with a view of forecasting the value of a new sample. A collaborative study between the Customer Laboratory and the Nutrition Department of AJINOMOTO EUROLYSINE S.A.S. is presented below. It shows how a database can be used in two different ways as a support for the prediction of amino acid contents in various feedstuffs categories AJINOMOTO EUROLYSINE S.A.S. Feedstuffs Database AJINOMOTO EUROLYSINE S.A.S. Customer Laboratory routinely analyzes feedstuffs and records the results in a database. This document is built on the basis of the results recorded since the year All total and native free amino acid contents, including tryptophan, have been determined in every sample, as well as nitrogen. Samples have been collected in Europe. They have been classified into feedstuffs categories, which might sound quite generic to the formulator (e.g. whey powders include sweet, acid, and non-specified products). Indeed precise description has not always been available at the time of sampling, with the result that larger categories may include sub-populations. Within-feedstuff variability is therefore magnified, compared to the variability that could be observed in one feed mill buying products on more restrictive specifications. An extended range of variations can however be considered as an advantage with regard to our objective of prediction. AJINOMOTO EUROLYSINE S.A.S. Feedstuffs Database includes more than 950 samples grouped in 44 feedstuffs categories At the date of writing, and after the withdrawal of outliers, AJINOMOTO EUROLYSINE S.A.S. Feedstuffs Database contains more than 950 samples, grouped in 44 feedstuffs categories. For clarification, a brief description of some categories is given below. Wheat: Most samples were identified as whole seeds. Some samples of processed wheat such as extruded wheat have also been included Corn: Most samples were identified as whole seeds. Some samples of processed corn (for very young piglets) have also been included. Triticale: All samples were identified as whole seeds, and most of them were collected in France in Rice: Samples were collected in Spain and include cooked rice. Wheat middlings: Samples were identified either as wheat middlings or wheat bran. Because ranges of crude protein (nitrogen * 6.25) and amino acid contents were almost completely overlapping, the two populations were put together. Wheat DDGS: Most samples were collected in France in 2007 and Corn DDGS: Samples were collected in Europe from 2009 to Information n 32 AJINOMOTO EUROLYSINE s.a.s.

23 Soybean meal: Samples were identified as soybean meal 43, 44, 46, 47, 48, 50 or high. The category includes toasted soybean meals. Samples were collected in Europe with various origins (when mentioned) such as Argentina, Brazil and Italy. Full fat soya: Samples were identified as whole seeds. When mentioned, process was dehulling, toasting, extrusion and/or expansion. The category includes genetically modified (GM) and non-gm seeds. Samples were collected in Europe with various origins including the US and Brazil. Soybean protein concentrate 52-56%: All samples show a crude protein content below 60%. This category includes products identified with 52%, 55% or 56% crude protein. Soybean protein concentrate 65%: All samples have a crude protein content above 60%. This category includes products identified with 65% crude protein. Sunflower meal: An important range of crude protein was observed among the 30 samples (from 25.4% to 39.4%), suggesting the existence of subgroups. Therefore a discriminant analysis taking into account the amino acid contents was performed and led to 3 categories: Sunflower meal 28%, Sunflower meal 33% and Sunflower meal 37%. Lupin seed: Samples were collected in Poland and Austria between 2011 and Potato protein: This category includes products identified with 76.5%, 78% or 79.5% crude protein. Milk: This category pools a large variety of dried dairy products. It includes buttermilk and milk powders, without further identification, or identified as delactosed, defatted or denatured. Whey powder: Samples were identified as whey powders, without indication or one or several of the following ones: sweet, acid, low lactose, delactosed, filled with fat. Fish meal: This category pools a large variety of products. Indications include herring meal, standard or low temperature or low ash, 70% or 72% crude protein. Origins include Chile, Peru, North Atlantic and Turkey Univariate Models Lead to Table Values Total Amino Acid Contents in 44 Feedstuffs Table of composition and withinfeedstuffs variability Table 4 on the first and final flaps presents the descriptive statistics for each individual amino acid in the 44 feedstuffs. Minimum and maximum contents (on % as fed basis) as well as coefficients of variation provide an insight of the within-feedstuffs variability. Moreover, the closeness in agreement between the arithmetic mean and the median is a sign that the distribution of samples may be Gaussian. For the prediction of the values of a new sample (not included in the dataset), it is common to use the average of the observations in the dataset. However the user could select the median, the mode (not shown), etc Mean Amino Acid Profiles Mean amino acid profiles and betweenfeedstuffs variability For feedstuffs comparison, the reader can refer to the amino acid profiles: each amino acid is expressed as a fraction of their sum (on a weight basis). Table 5 on pages presents the mean amino acid profile of the 44 feedstuffs. Soya products (full fat soya, soybean meal and various soybean protein concentrates) show very similar profiles. Dairy products on the contrary have contrasted profiles. Indeed, the proteins of the whey fraction of milk (lactoglobulines and lactalbumines) contain more lysine, threonine, cystine and tryptophan than caseins, resulting in a more interesting amino acid profile from a nutritional standpoint. AJINOMOTO EUROLYSINE s.a.s. Information n 32 19

24 Amino acid profile vs. protein profile In publications from other authors (Sauvant et al., 2004 for instance), amino acid profiles are generally expressed as a percentage of crude protein (CP), defined as nitrogen content * The purpose is to give a practical tool to adapt the amino acid contents (on % as fed basis) once nitrogen has been measured in a sample. Focus A nutritionnist s point of view In ingredients however, nitrogen from the proteins only accounts for a fraction of the total nitrogen content, and this fraction depends on the feedstuffs. Profiles established on CP under-estimate the amino acid contributions of all amino acids in feedstuffs where the non-protein fraction is considerably high (e.g. fish meal) and are therefore not adapted for feedstuffs comparisons. For the same reason, profiles from different tables (established on CP or on the sum of all amino acids) should be compared with care. Although not recommended, our profile can be converted into a profile based on CP, using mean CP contents reported in Table 8 on page 24. For simplification, only mean amino acid profiles are given in the document. It is clear however that intra-feedstuffs variations exist. In wheat for example, the global amino acid profile depends on the relative proportions of the major storage proteins (glutenins, gliadins, albumines and globulines). Grains with higher protein contents have more gluten proteins (Wieser and Seilmeier, 1998), which are particularly rich in glutamic acid and glutamine, but poor in lysine. Therefore, lysine content, expressed as a fraction of the sum of all amino acids, is not constant. It decreased as crude protein increases (Figure 6). Total Lysine, % sum of all amino acids 3.7% 3.5% 3.3% 3.1% 2.9% 2.7% R 2 = % Crude protein (Nitrogen * 6.25), % as fed basis Source: AJINOMOTO EUROLYSINE S.A.S. laboratory analysis database, as of January Figure 6: Negative correlation between crude protein content (% as fed basis) and lysine content (% of all amino acids) in wheat (each dot represents 1 of the 114 wheat samples). 20 Information n 32 AJINOMOTO EUROLYSINE s.a.s.

25 Native Free Amino Acid Contents α-amino acids are mainly but not exclusively included in peptidic chains in feedstuffs In feedstuffs, amino acids are mainly included in proteins, but a small fraction, generally less than 2%, is present in a free form. Native free lysine, threonine and methionine contents in all 44 feedstuffs are low (Table 6, on pages 16-17). The supply from feedstuffs is negligible, compared to usual supplementation levels. Consequently, the analysis of free amino acids in a diet supplemented with feed-use amino acids will give a good estimate of lysine, threonine and methionine supplementation. Considerable amounts of native free tryptophan (completely free or bound to albumin for circulation) can be found in wheat (grains and by-products) and in soya. It is therefore recommended to take into account the native content of the feedstuffs when L-tryptophan supplementation is estimated based on free tryptophan analysis. Table 6 provides information on the native free tryptophan levels observed in the 44 feedstuffs. It can be noted that some feedstuffs also contain considerable amounts of native free arginine. Conversely, the native free fractions of valine (Table 6) and isoleucine (data not shown) are generally negligible Bivariate Models Lead to Predictive Equations Amino acid profiles of ingredients are affected by changes in the relative proportions of the constitutive proteins. Amino acid levels do not vary independently. This characteristic can be used to improve predictions Correlations Between Amino Acid Contents Correlations between amino acid contents can be observed in various feedstuffs categories For the 23 feedstuffs with at least 6 samples, correlations were searched between the individual amino acids (153 correlations per feedstuff in total). The different groups of sunflower meals were considered together. Positive and high correlation coefficients (r > 0.80) were found for almost all amino acids in the following ingredients: wheat, barley, milk and whey powder (Figure 7). On the contrary, poor correlation coefficients ( r < 0.50) were found for most amino acids in the following ingredients: whey protein concentrate, wheat gluten, wheat middlings, wheat DDGS, full fat soya, soya protein concentrates 52-56%, corn, fish meal, rape seed meal, lupin, brewers yeast and corn gluten. The remaining ingredients (triticale, soybean meal, soya protein concentrates 65%, sunflower meal, corn DDGS, potato protein and peas) showed variable correlation coefficients depending on the amino acids considered. At least two reasons might explain the poor correlations. First, industrial products like potato protein, wheat gluten or corn gluten have standardized specifications. Since nitrogen content varies within a short range, it is difficult to characterize variability. At the opposite, some categories like fish meal contain very different products. In the absence of adequate information, it was not possible to distinguish sub-populations. Some correlations might exist in sub-populations, but have been masked on a global basis. It is well known for example that fish meal made from tuna contains more histidine than others. Figure 7, on page 22, shows how correlation between valine and histidine appears when samples with high histidine contents are excluded from the dataset. It can be concluded that the absence of correlations between amino acids in some feedstuffs should not be regarded as an internal characteristic of the ingredients but may result from dataset artifacts. AJINOMOTO EUROLYSINE s.a.s. Information n 32 21

26 0.45 Wheat (n=114) Total Threonine, % as fed basis 4.0 Fish meal (n=51) Total Valine, % as fed basis R 2 = Total Lysine, % as fed basis Total Histidine, % as fed basis Source: AJINOMOTO EUROLYSINE S.A.S. Feedstuffs Database, as of January Figure 7: Correlations between amino acids are clearly apparent in wheat but not in fish meal Linear Regressions with Nitrogen as Independent Variable Correlations between nitrogen and amino acid contents have been used to set up predictive regressions It was hypothesized that if amino acid contents are correlated to each other, significant correlations may exist between individual amino acid contents and their sum, or between individual amino acid and nitrogen (N) contents. Indeed, based on our dataset, feedstuffs with high correlations between amino acids also showed high correlations between N and individual amino acid contents. For the feedstuffs with clear correlations between N and most individual amino acids, regression analysis was performed using the method of least squares. Amino acids are nitrogenous compounds, therefore from a causal standpoint, amino acid contents should be considered as the explanatory variables for nitrogen contents. In practice however, it is more interesting to explore the opposite relationship with a view of forecasting the amino acid contents using data of a routinely-checked analyte. Data were submitted to linear regressions with N content as independent variable and individual amino acids as dependent variables. A few samples with extremely low or high nitrogen content have been excluded because they would have had a too high of weight in the regressions. Normality of observations and homoscedasticity of the residuals were checked. When the statistical hypothesis: a coefficient of the model (slope or intercept) is zero could not be rejected, the coefficient was withdrawn from the model. In total, acceptable regressions were obtained for twelve feedstuffs. The corresponding estimates for the slopes and intercepts are given in Table 7 on pages Information n 32 AJINOMOTO EUROLYSINE s.a.s.

27 2.4. Risk Management in Feedstuffs Evaluation Two models have been established, based on the same dataset: The first one consists in table values, whereas the second one gives two linear regression coefficients in order to derive amino acid contents from nitrogen content. The final objective is to use these models to forecast the amino acid contents of samples that were not included in the database (external samples vs. internal samples). A method is proposed to assess and compare the quality of predictions Precision of Predictions Based on Table Values Calculation of the confidence interval of the prediction based on table values The mean values of the dataset have been selected as predictors of any external sample. The confidence interval of the mean is defined as +/ (for the probability level of 0.05) multiplied by the standard deviation and divided by the root of the number of observations. The confidence interval of the prediction however is +/ * standard deviation. A greater number of observations drastically improves the precision of the mean, with subsequent reduction of the bias of prediction, but has no effect on the precision of predictions. The following example is illustrated in Figure 8: AJINOMOTO EUROLYSINE S.A.S. Feedstuffs Database contains 114 samples of wheat. The confidence interval of prediction (in green) is thus 11 times wider than the confidence interval of the mean values of the database (in purple). For any new sample of wheat, the predicted lysine content will be 0.32% +/- 0.05%. Number of samples per class Table model: Y = µ + ε Prediction: Data: n=114 Lys analyses Mean ± 1.96 * std / n of internal samples 0.32 ± 0.01 Estimate ± 1.96 * std for any external sample 0.32 ± 0.05 < 0.25 ] ] ] ] ] ] ] ] ] ] ] ] ] ] > 0.39 Total Lysine, % as fed basis Estimated value Confidence interval (P = 0.05) Figure 8: Precision of the prediction based on table value (confidence interval). Example of lysine in wheat. AJINOMOTO EUROLYSINE s.a.s. Information n 32 23

28 Precision of Predictions Based on Linear Regressions Validity interval: range of nitrogen contents Extrapolation is not recommended; therefore linear regressions should not be applied for nitrogen contents outside the range in the database (Table 8). Crude protein equivalents are also given for practical understanding. Nitrogen (N) % Crude Protein % = N * 6.25 n Min Max Median Mean Std CV Min Max Median Mean Std CV Cereals Cereals by-products Vegetable protein sources Dairy products Miscellaneous Wheat % % Barley % % Corn % % Triticale % % Oats % % Rice % % Rye % % Sorghum Wheat middlings & bran % % Wheat gluten % % Wheat gluten feed % % Wheat DDGS % % Corn feed flour Corn gluten meal 60 % % % Corn germ Corn DDGS % % Rice protein Soybean meal % % Full fat soybean % % Soya protein concentrate % % % Soya protein concentrate 65 % % % Rape seed meal % % Full fat rape seed Sunflower meal 28 % % % Sunflower meal 33 % % % Sunflower meal 37 % % % Palm kernel meal Faba bean Lupin seed % % Pea % % Potato protein concentrate % % Milk % % Whey powder % % Whey protein concentrate % % Yoghurt Fish meal % % Blood meal Feather meal % % Poultry protein Plasma % % Egg % % Cassava Brewers' yeast % % Bakery by-products % % Source: AJINOMOTO EUROLYSINE S.A.S. laboratory analysis database, as of January All nitrogen analyses have been performed in AJINOMOTO EUROLYSINE S.A.S. Customer Laboratory (Amiens, France) following Dumas method. Table 8: Nitrogen content of 44 ingredients (% as fed basis) and multiplication by Information n 32 AJINOMOTO EUROLYSINE s.a.s.

29 Calculation of the prediction interval associated with the linear regressions A high value for the coefficient of determination R² is necessary but not sufficient condition to ensure the quality of a regression. In our dataset, R² was often at least 0.99, while a great number of observations were not on the regression line. That is why attention was paid to two other parameters: the confidence interval and the prediction interval. For each amino acid of each ingredient, slope (a) and intercept (b) of the linear regression have been estimated. These estimates, reported in Table 7 on pages 32-35, are defined with a confidence interval. Consequently, the regression itself fluctuates within a confidence interval (materialised as the purple area in Figure 9). Amino acid contents of a new sample will be predicted using the estimated coefficients, and the prediction will lie in the prediction interval. The prediction limits are two hyperboles, with the highest distance for extremely low and high nitrogen contents. Every estimate has a different prediction interval, but for simplification, an upper bound, valid on the total nitrogen range, is given in Table 7. The prediction interval is shown as a green band on Figure 9. Figure 9 illustrates the case of lysine in wheat. In total 114 pairs of data have been used to build the linear regression. For a sample with 1.8% nitrogen (11.2% crude protein as N * 6.25), the estimated lysine content will be 0.32% +/- 0.03%. For a sample with 1.6% nitrogen (10.0% crude protein), the result will be 0.29% +/- 0.03%. Data: n=114 pairs of analyses [N ; Lys] Total Lysine, % as fed basis y = * x R 2 = 0.78 Confidence interval of the regression model Prediction interval for samples which were not included in the dataset Nitrogen, % as fed basis Source: AJINOMOTO EUROLYSINE S.A.S. laboratory analysis database, as of January Figure 9: Precision of the prediction based on linear regression (prediction interval). Example of lysine in wheat. AJINOMOTO EUROLYSINE s.a.s. Information n 32 25

30 Compared Precision of Table Values, Predictive Equations and Analyses The use of predictive equations results in variable estimates compared to the fixed table value. Figure 10 illustrates the fact that the knowledge of nitrogen contents improves predictions, especially when nitrogen content is particularly low or high. Total Lysine, % 0.45 True (but unknown!) value Estimated by Regression on N 0.30 Estimated by Table Value (in case N is not measured) Nitrogen, % Figure 10: Prediction of lysine content of eight wheat samples based on table value or regression. Reduced risk of misevaluation of the ingredients with the narrowest confidence intervals Linear regressions improve precision of forecasts compared to table values: In the case of lysine in wheat, the precision has almost doubled (prediction interval was decreased from +/ to +/- 0.03%). Using nitrogen as the independent variable will improve not only the estimation of lysine, but the estimation of all amino acids. Figure 11 illustrates in three dimensions the precision of forecasts based on analyses (smallest sphere in grey), linear regressions (medium sphere in green) and table values (largest sphere in brown). The radiuses of the spheres represent the prediction intervals (ex: +/-3%, 9% and 19% for all amino acids on average in wheat, for analyses, regressions and table values, respectively). Thr Area of the Table values Area of the Regressions on N Area of the Analyses True (but unknown!) value of an external sample Lys Trp Figure 11: A schematic 3D-representation of the precision of estimations based on table values, linear regressions and analyses. 26 Information n 32 AJINOMOTO EUROLYSINE s.a.s.

31 Conclusion Which Amino Acid Values for the Formulation Matrix? Guidance on how to use the tables provided in this handbook It is important for any feed formulator to possess a matrix of ingredients which reflects the nutritional values of the feedstuffs batches available in the feed mill. Ingredient compositions should be evaluated when changes are expected (new crop, new supplier, etc.). Direct estimation through amino acid analysis is recommended but hardly feasible in routine. Therefore two different tools were discussed in this document: Table values provide basic composition data, which are valuable for inter-feedstuffs comparisons; linear regressions provide more precise data, useful to make the best use of feedstuffs. The quality of prediction based on table values and on regressions depends on the feedstuffs. Table values are acceptable for soybean meal, full fat soybean, soya protein concentrate 65%, rapeseed meal, and sunflower meal. Cereals are likely to be more variable. It is therefore suggested to regularly analyze the amino acid contents of new batches, or at least to analyze nitrogen contents and derive amino acid contents using regressions. In order to help the formulator to select the best evaluation tools, Table 9 summarizes the relative confidence intervals of the predictions based on table values, regressions and analyses for the twelve feedstuffs where satisfactory regressions could be established. For all other feedstuffs, table values provide basic data but analysis should be preferred (Tables 10 and 11, page 28). Table 1 Regressions Analysis 2 Wheat Relative confidence intervals Barley < 10% Corn 10-30% Triticale > 30% Soybean meal Recommended solutions Full fat soybean Number 1 Soya protein concentrate 65 % Number 2 Rapeseed meal Not recommended Sunflower meal Pea Milk Whey powder 1) Table values and regressions by AJINOMOTO EUROLYSINE S.A.S. (Table 7) 2) Based on intra-laboratory reproducibility data of AJINOMOTO EUROLYSINE S.A.S. Customer Laboratory Table 9: Efficiency of three methods of prediction of amino acid contents in twelve feedstuffs (table values, regressions on nitrogen and amino acid analyses). AJINOMOTO EUROLYSINE s.a.s. Information n 32 27

32 Table 1 Regressions Analysis 2 Wheat middlings and bran Wheat gluten Wheat DDGS Poor Corn gluten meal 60 % Corn DDGS Soya protein concentrate % Lupin seed Poor Potato protein concentrate Relative confidence intervals < 10% 10-30% > 30% Recommended solutions Number 1 Number 2 Not recommended Whey protein concentrate Poor Fish meal Poor Brewers's yeast 1) Table values by AJINOMOTO EUROLYSINE S.A.S. (Table 7) 2) Based on intra-laboratory reproducibility data of AJINOMOTO EUROLYSINE S.A.S. Customer Laboratory Table 10: Efficiency of three methods of prediction of amino acid contents in eleven feedstuffs with poor regressions. Table 1 Regressions Analysis 2 Oats Rice Rye Sorghum Few samples Not tested Relative confidence intervals < 10% 10-30% > 30% Wheat gluten feed Corn feed flour Corn germ Rice protein Few samples Not tested Recommended solutions Number 1 Number 2 Not recommended Full fat rape seed Palm kernel meal Faba bean Few samples Not tested Yoghurt Few samples Not tested Blood meal Feather meal Poultry protein Plasma Egg Cassava Bakery by-products Few samples Not tested 1) Table values by AJINOMOTO EUROLYSINE S.A.S. (Table 7). 2) Based on intra-laboratory reproducibility data of AJINOMOTO EUROLYSINE S.A.S. Customer Laboratory. Table 11: Efficiency of three methods of prediction of amino acid contents in nineteen feedstuffs with no regressions tested due to the too small number of samples. 28 Information n 32 AJINOMOTO EUROLYSINE s.a.s.

33 Reference list n AFNOR. Accuracy (trueness and precision) of measurement methods and results. Part 1: General principles and definitions. NF ISO n AFNOR. Accuracy (trueness and precision) of measurement methods and results. Part 2: Basic methods for the determination of repeatability and reproducibility of a standard measurement method. NF ISO n AFNOR. Accuracy (trueness and precision) of measurement methods and results. Part 3: Intermediate measures of the precision of a standard measurement method. NF ISO n AFNOR. Accuracy (trueness and precision) of measurement methods and results. Part 4: Basic methods for the determination of the trueness of a standard measurement method. NF ISO n AFNOR. Accuracy (trueness and precision) of measurement methods and results. Part 5: Alternative methods for the determination of the precision of a standard measurement method. NF ISO n AFNOR. Accuracy (trueness and precision) of measurement methods and results. Part 6: Use in practice of accuracy values. NF ISO n AFNOR. Animal Feeding Stuffs Determination of the total nitrogen content by combustion according to the Dumas principle and calculation of the crude protein content. Part 1: Oilseeds and animal feedingstuffs. EN ISO n AFNOR. Animal Feeding Stuffs Determination of tryptophan. NF V n AFNOR. Statistical methods for use in proficiency testing by interlaboratory comparisons. NF ISO n AFNOR. General requirements for the competence of testing and calibration laboratories (ISO/IEC 17025:2005). NF EN ISO/CEI n AFNOR. Animal Feeding Stuffs Sampling. NF EN ISO n Commission Regulation N 152/2009 of 27 January 2009 laying down the methods of sampling and analysis for the official control of feeds (Official Journal of the European Union L 54/1 of ) n Council Directive. 79/373/EEC of 2 April 1979 on the M15 circulation of compound feedingstuffs (Official Journal of the European Communities. L86 of ) n Primot, Y. and D. Melchior Tryptophan in young pigs: An essential nutrient with numerous biological functions. AJINOMOTO EUROLYSINE S.A.S. Technical information n 30. n Sauvant, D., J. M. Perez, and G. Tran Tables of composition and nutritional value of feed materials. Wageningen Academic Publishers, INRA Editions and AFZ, Paris. n Wesseling, B. and F. Liebert. Untersuchungen zum Vergleich von Aminosäurenwirksamkeit und ilealer Verdaulichkeit von Aminosäuren in Bezug zur gemessenen Leistung (investigations comparing diet formulation based on amino acid efficiency and ileal amino acid digestibility relating to performance). 7 Tagung Schweine Und Geflügelernährung, n Wieser, H. and W. Seilmeier The influence of nitrogen fertilisation on quantities and proportions of different protein types in wheat flour. J.Sci.Food.Agric. 76, AJINOMOTO EUROLYSINE s.a.s. Information n 32 29

34 The 20 proteinogenic amino acids directly encoded by the universal genetic code The proteinogenic amino acids are precursors of proteins. The amino acids sequence of a protein is dictated by a linear sequence of a continuous triplet of nucleotides (CODON) and determines the structure and the role of this protein. Name Abbreviation Formula Molar mass 2, g/mol Nitrogen, % Focus An analyst s and a nutritionist s points of view Lysine Lys C 6H 14N 2O % Threonine Thr C 4H 9NO % Methionine Met C 5H 11NO 2S 149 9% Cysteine 1 Cys C 3H 7NO 2S % Tryptophan Trp C 11H 12N 2O % Valine Val C 5H 11NO % Isoleucine Ile C 6H 13NO % Leucine Leu C 6H 13NO % Arginine Arg C 6H 14N 4O % Phenylalanine Phe C 9H 11NO % Tyrosine Tyr C 9H 11NO % Histidine His C 6H 9N 3O % Serine Ser C 3H 7NO % Alanine Ala C 3H 7NO % Aspartic acid Asp C 4H 7NO % Asparagine Asn C 4H 8N 2O % Glutamic acid Glu C 5H 9NO % Glutamine Gln C 5H 10N 2O % Glycine Gly C 2H 5NO % Proline Pro C 5H 9NO % 1) Cystine is the amino acid formed by the oxidation of 2 cysteine molecules, linked via a disulfide bond. Consequently, its formula is C6H12N2O4S2. 2) Molar mass of Carbon (C) is 12 g/mol, of Hydrogen (H) 1 g/mol, of Oxygen (O) 16 g/mol, of Nitrogen (N) 14 g/mol and of Sulfur (S) 32 g/mol. Example: Lysine molar mass = 6 * * * * 16 = 146 g/mol. Lysine Nitrogen content = (2 * 14) / 146 = 19%. Legend Carbon Hydrogen Oxygen Nitrogen Sulfur 30 Information n 32 AJINOMOTO EUROLYSINE s.a.s.

35 Glycine Alanine Valine Leucine Isoleucine Phenylalanine Tyrosine Tryptophan Lysine Arginine Histidine Aspartic acid Glutamic acid Asparagine Glutamine Cysteine Methionine Serine Threonine Proline AJINOMOTO EUROLYSINE s.a.s. Information n 32 31

36 WHEAT Using table Using regression AA (%) = a x N (%) + b Content 1 % Confidence 2 % Relative 3 a b Prediction 4 % Relative 3 ΔP 5 Lys % % x 2.0 Thr % % x 3.4 Met % % x 2.5 Cys % % x 2.4 Trp % % x 1.6 Val % % x 3.5 Ile % % x 4.1 Leu % % x 6.2 Arg % % x 2.4 Phe % % x 4.0 Tyr % % x 1.6 His % % x 3.1 Ser % % x 4.2 Ala % % x 3.3 Asp* % % x 2.5 Glu* % % x 3.9 Gly % % x 3.5 Pro % % x 2.6 BARLEY Using table Using regression AA (%) = a x N (%) + b Content 1 % Confidence 2 % Relative 3 a b Prediction 4 % Relative 3 ΔP 5 Lys % % x 2.1 Thr % % x 4.2 Met % % x 4.0 Cys % % x 2.2 Trp % % x 3.0 Val % % x 3.9 Ile % % x 4.4 Leu % % x 5.9 Arg % % x 2.9 Phe % % x 4.5 Tyr % % x 2.1 His % % x 3.5 Ser % % x 4.6 Ala % % x 2.8 Asp* % % x 2.4 Glu* % % x 3.7 Gly % % x 3.0 Pro % % x 3.1 CORN Using table Using regression AA (%) = a x N (%) + b Content 1 % Confidence 2 % Relative 3 a b Prediction 4 % Relative 3 ΔP 5 Lys % % x 1.0 Thr % % x 2.4 Met % % x 1.2 Cys % % x 1.2 Trp % % x 1.2 Val % % x 2.7 Ile % % x 2.7 Leu % % x 2.8 Arg % % x 1.2 Phe % % x 2.5 Tyr % % x 1.9 His % % x 1.8 Ser % % x 2.9 Ala % % x 2.9 Asp* % % x 2.0 Glu* % % x 2.9 Gly % % x 1.3 Pro % % x 2.1 Table 7 Part 1: Prediction of amino acid contents (% as fed basis) in 6 ingredients using either table means or linear regressions: proposed values and compared accuracy of the estimations. In the tables, a and b values have been multiplied by 1000 for better reading. 32 Information n 32 AJINOMOTO EUROLYSINE s.a.s.

37 Using table TRITICALE Using regression AA (%) = a x N (%) + b Content 1 % Confidence 2 % Relative 3 a b Prediction 4 % Relative 3 ΔP 5 Lys % % x 1.0 Thr % % x 1.7 Met % % x 1.3 Cys % % x 1.3 Trp % % x 1.0 Val % % x 1.9 Ile % % x 2.2 Leu % % x 2.7 Arg % % x 1.2 Phe % % x 2.1 Tyr % % x 1.2 His % % x 1.7 Ser % % x 2.6 Ala % % x 1.2 Asp* % % x 1.1 Glu* % % x 2.4 Gly % % x 1.2 Pro % % x 1.3 Using table SOYBEAN MEAL Using regression AA (%) = a x N (%) + b Content 1 % Confidence 2 % Relative 3 a b Prediction 4 % Relative 3 ΔP 5 Lys % % x 2.1 Thr % % x 2.1 Met % % x 1.6 Cys % % x 1.3 Trp % % x 1.5 Val % % x 2.0 Ile % % x 2.2 Leu % % x 2.5 Arg % % x 2.5 Phe % % x 2.3 Tyr % % x 2.2 His % % x 2.2 Ser % % x 2.5 Ala % % x 2.3 Asp* % % x 2.9 Glu* % % x 2.7 Gly % % x 2.2 Pro % % x 1.3 Using table FULL FAT SOYBEAN Using regression AA (%) = a x N (%) + b Content 1 % Confidence 2 % Relative 3 a b Prediction 4 % Relative 3 ΔP 5 Lys % % x 1.7 Thr % % x 1.1 Met % % x 1.0 Cys % % x 1.1 Trp % % x 1.3 Val % % x 2.5 Ile % % x 2.2 Leu % % x 3.0 Arg % % x 2.4 Phe % % x 2.2 Tyr % % x 1.7 His % % x 2.1 Ser % % x 2.5 Ala % % x 2.3 Asp* % % x 2.3 Glu* % % x 1.9 Gly % % x 2.4 Pro % % x 1.2 1) The amino acid contents are estimated by the mean content of the samples in the database. 2) The limits of the confidence interval at the 0.05 probability level are ± 1.96 * standard error of the samples of the database. 3) The ratio between the limit of the confidence 2 or prediction 4 interval and the estimated content. 4) Upper bound of the prediction interval of the regression at the 0.05 probability level. 5) The change in precision (ΔP) is calculated as the ratio between the prediction interval 4 and the confidence interval 2. Source: AJINOMOTO EUROLYSINE S.A.S. laboratory analysis database, as of January * Due to the analytical procedure, aspartic acid (Asp) and glutamic acid (Glu) represent the total amounts, aspartic acid (Asp) plus asparagine (Asn), and glutamic acid (Glu) plus glutamine (Gln), respectively. AJINOMOTO EUROLYSINE s.a.s. Information n 32 33

38 SOYA PROTEIN CONCENTRATE 65 % Using table Using regression AA (%) = a x N (%) + b Content 1 % Confidence 2 % Relative 3 a b Prediction 4 % Relative 3 ΔP 5 Lys % % x 1.6 Thr % % x 1.5 Met % % x 1.3 Cys % % x 0.9 Trp % % x 0.7 Val % % x 1.7 Ile % % x 1.6 Leu % % x 2.3 Arg % % x 2.3 Phe % % x 1.6 Tyr % % x 1.7 His % % x 1.5 Ser % % x 1.9 Ala % % x 1.8 Asp* % % x 1.7 Glu* % % x 2.7 Gly % % x 2.1 Pro % % x 1.2 RAPE SEED MEAL Using table Using regression AA (%) = a x N (%) + b Content 1 % Confidence 2 % Relative 3 a b Prediction 4 % Relative 3 ΔP 5 Lys % % x 1.0 Thr % % x 1.4 Met % % x 1.6 Cys % % x 1.3 Trp % % x 1.3 Val % % x 1.5 Ile % % x 1.4 Leu % % x 1.2 Arg % % x 1.4 Phe % % x 1.4 Tyr % % x 1.3 His % % x 1.4 Ser % % x 1.8 Ala % % x 1.4 Asp* % % x 1.3 Glu* % % x 1.7 Gly % % x 1.7 Pro % % x 1.2 SUNFLOWER MEAL Using table Using regression AA (%) = a x N (%) + b Content 1 % Confidence 2 % Relative 3 a b Prediction 4 % Relative 3 ΔP 5 Lys % % x 1.7 Thr % % x 1.7 Met % % x 0.9 Cys % % x 2.2 Trp % % x 1.1 Val % % x 1.2 Ile % % x 1.1 Leu % % x 1.7 Arg % % x 1.2 Phe % % x 1.2 Tyr % % x 1.2 His % % x 1.4 Ser % % x 1.3 Ala % % x 1.1 Asp* % % x 1.3 Glu* % % x 1.5 Gly % % x 1.2 Pro % % x 1.1 Table 7 Part 2: Prediction of amino acid contents (% as fed basis) in 6 ingredients using either table means or linear regressions: proposed values and compared accuracy of the estimations. In the tables, a and b values have been multiplied by 1000 for better reading. 34 Information n 32 AJINOMOTO EUROLYSINE s.a.s.

39 Using table PEA Using regression AA (%) = a x N (%) + b Content 1 % Confidence 2 % Relative 3 a b Prediction 4 % Relative 3 ΔP 5 Lys % % x 1.6 Thr % % x 1.5 Met % % x 0.9 Cys % % x 0.5 Trp % % x 1.1 Val % % x 1.7 Ile % % x 2.1 Leu % % x 2.3 Arg % % x 2.8 Phe % % x 2.5 Tyr % % x 1.3 His % % x 1.9 Ser % % x 2.6 Ala % % x 1.5 Asp* % % x 2.2 Glu* % % x 1.7 Gly % % x 1.0 Pro % % x 1.1 Using table MILK Using regression AA (%) = a x N (%) + b Content 1 % Confidence 2 % Relative 3 a b Prediction 4 % Relative 3 ΔP 5 Lys % % x 3.0 Thr % % x 4.6 Met % % x 1.6 Cys % % x 1.2 Trp % % x 6.2 Val % % x 2.8 Ile % % x 5.1 Leu % % x 3.6 Arg % % x 1.4 Phe % % x 3.6 Tyr % % x 2.5 His % % x 3.3 Ser % % x 4.4 Ala % % x 3.1 Asp* % % x 2.4 Glu* % % x 4.3 Gly % % x 1.4 Pro % % x 1.5 Using table WHEY POWDER Using regression AA (%) = a x N (%) + b Content 1 % Confidence 2 % Relative 3 a b Prediction 4 % Relative 3 ΔP 5 Lys % % x 3.5 Thr % % x 2.9 Met % % x 1.6 Cys % % x 2.5 Trp % % x 3.9 Val % % x 3.4 Ile % % x 4.5 Leu % % x 4.3 Arg % % x 1.7 Phe % % x 2.1 Tyr % % x 1.9 His % % x 2.2 Ser % % x 3.8 Ala % % x 3.5 Asp* % % x 4.1 Glu* % % x 2.2 Gly % % x 2.4 Pro % % x 1.4 1) The amino acid contents are estimated by the mean content of the samples in the database. 2) The limits of the confidence interval at the 0.05 probability level are ± 1.96 * standard error of the samples of the database. 3) The ratio between the limit of the confidence 2 or prediction 4 interval and the estimated content. 4) Upper bound of the prediction interval of the regression at the 0.05 probability level. 5) The change in precision (ΔP) is calculated as the ratio between the prediction interval 4 and the confidence interval 2. Source: AJINOMOTO EUROLYSINE S.A.S. laboratory analysis database, as of January * Due to the analytical procedure, aspartic acid (Asp) and glutamic acid (Glu) represent the total amounts, aspartic acid (Asp) plus asparagine (Asn), and glutamic acid (Glu) plus glutamine (Gln), respectively. AJINOMOTO EUROLYSINE s.a.s. Information n 32 35

40 Lysine % Threonine % n Min Max Median Mean Std CV Min Max Median Mean Std CV Vegetable protein sources Dairy products Miscellaneous Soybean meal % % Full fat soybean % % Soya protein concentrate % % % Soya protein concentrate 65 % % % Rape seed meal % % Full fat rape seed Sunflower meal 28 % % % Sunflower meal 33 % % % Sunflower meal 37 % % % Palm kernel meal Faba bean Lupin seed % % Pea % % Potato protein concentrate % % Milk % % Whey powder % % Whey protein concentrate % % Yoghurt Fish meal % % Blood meal Feather meal % % Poultry protein Plasma % % Egg % % Cassava Brewers yeast % % Bakery by-products % % Phenylalanine % Tyrosine % n Min Max Median Mean Std CV Min Max Median Mean Std CV Vegetable protein sources Dairy products Miscellaneous Soybean meal % % Full fat soybean % % Soya protein concentrate % % % Soya protein concentrate 65 % % % Rape seed meal % % Full fat rape seed Sunflower meal 28 % % % Sunflower meal 33 % % % Sunflower meal 37 % % % Palm kernel meal Faba bean Lupin seed % % Pea % % Potato protein concentrate % % Milk % % Whey powder % % Whey protein concentrate % % Yoghurt Fish meal % % Blood meal Feather meal % % Poultry protein Plasma % % Egg % % Cassava Brewers yeast % % Bakery by-products % % Table 4 Part 2: Amino acid contents of Vegetable protein sources, Dairy products and Miscellaneous (% as fed basis). For Cereals and Cereals by-products, report to Table 4 Part 1 (first flap).

41 Methionine % Cystine % Tryptophan % Valine % Min Max Median Mean Std CV Min Max Median Mean Std CV Min Max Median Mean Std CV Min Max Median Mean Std CV Amino acids online database % % % % % % % % % % % % % % % 2.96 Visit our online feedstuffs amino acids database on and access to: % The 0.02 amino 3% acid content 0.68 of 0.84 the ingredients described 6% in 0.39 Table % % The amino acid content 0.44 prediction 0.44 tool 0.44based on Nitrogen 0.25 and 0.32 presented in 0.28 Table % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % Histidine % Serine % Alanine % Aspartic acid * % Min Max Median Mean Std CV Min Max Median Mean Std CV Min Max Median Mean Std CV Min Max Median Mean Std CV % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % Disclaimer The 0.90 document % and the 1.16original 2.19 data 1.55 contained therein 18% are the 0.97property of: % % AJINOMOTO % EUROLYSINE S.A.S % % % Registered office: 4% rue 4.57 de Courcelles, Paris 4% Cedex , 4.26 France 3.84 Tel.: Fax: % % % % % % The 0.22 company % AJINOMOTO EUROLYSINE S.A.S has 43% taken all 0.21 steps 1.21 to check 0.51the 0.57 authenticity % and relevance 0.41 of 2.61 the information % provided in this 9% bulletin. 1.57However, AJINOMOTO EUROLYSINE 6% 1.53S.A.S declines 1.68 all 1.75 responsibility % for any 3.23 use made 3.94 of 3.73 this 3.63 data % and 0.93 may not, under any 1.75 circumstances, be held liable for any 1.50damage 1.68 suffered 1.59 by third parties % % % % % % % % % % % % % % % % % % % % % % % %