SIST EN 15948:2015
(Main)Cereals - Determination of moisture and protein - Method using Near-Infrared-Spectroscopy in whole kernels
Cereals - Determination of moisture and protein - Method using Near-Infrared-Spectroscopy in whole kernels
This European Standard defines a routine method for the determination of moisture and protein in whole kernels of barley and wheat using a near-infrared spectrophotometer in the constituent ranges:
a) for wheat:
1) moisture content minimum range from 8 % to 22 %;
2) protein content minimum range from 7 % to 20 %.
b) for barley:
1) moisture content minimum range from 8 % to 22 %;
2) protein content minimum range from 7 % to 16 %.
This European Standard describes the modalities to be implemented by the supplier (5.3 and 5.4) and the user of the method.
Getreide - Bestimmung der Feuchte und des Proteins - Verfahren der Nahinfrarot-Spektroskopie bei ganzen Körnern
Diese Europäische Norm legt ein routinemäßiges Verfahren für die Bestimmung der Feuchte und des Proteins in ganzen Gersten- und Weizenkörnern unter Anwendung eines Nahinfrarot-Spektrophotometers in den folgenden Bestandteilbereichen fest:
a) Für Weizen:
1) Feuchtegehalt mindestens von 8 % bis 22 %;
2) Proteingehalt mindestens von 7 % bis 20 %.
b) Für Gerste:
1) Feuchtegehalt mindestens von 8 % bis 22 %;
2) Proteingehalt mindestens von 7 % bis 16 %.
In der vorliegenden Europäischen Norm sind die Modalitäten beschrieben, die vom Lieferanten (5.3 und 5.4) und vom Anwender des Verfahrens umzusetzen sind.
Céréales - Détermination de la teneur en eau et en protéines - Méthode utilisant la spectroscopie dans le proche infrarouge sur des grains entiers
La présente Norme européenne définit une méthode de routine pour la détermination de la teneur en eau et en protéines dans des grains entiers d’orge et de blé, à l’aide d’un spectromètre proche infrarouge, dans les plages suivantes :
a) pour le blé :
1) teneur en eau comprise dans une plage minimale de 8 % à 22 % ;
2) teneur en protéines comprise dans une plage minimale de 7 % à 20 %.
b) pour l’orge :
1) teneur en eau comprise dans une plage minimale de 8 % à 22 % ;
2) teneur en protéines comprise dans une plage minimale de 7 % à 16 %.
La présente Norme européenne décrit les modalités à mettre en œuvre par le fournisseur (5.3 et 5.4) et par l’utilisateur de la méthode.
Žito - Določevanje vlage in beljakovin - Metoda z uporabo bližnje infrardeče spektroskopije v celih zrnih
General Information
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Standards Content (Sample)
2003-01.Slovenski inštitut za standardizacijo. Razmnoževanje celote ali delov tega standarda ni dovoljeno.Getreide - Bestimmung der Feuchte und des Proteins - Verfahren der Nahinfrarot-Spektroskopie bei ganzen KörnernCéréales - Détermination de la teneur en eau et en protéines - Méthode utilisant la spectroscopie dans le proche infrarouge sur des grains entiersCereals - Determination of moisture and protein - Method using Near-Infrared-Spectroscopy in whole kernels67.060QMLKCereals, pulses and derived productsICS:Ta slovenski standard je istoveten z:EN 15948:2015SIST EN 15948:2015en,fr,de01-junij-2015SIST EN 15948:2015SLOVENSKI
STANDARDSIST EN 15948:20121DGRPHãþD
SIST EN 15948:2015
EUROPEAN STANDARD NORME EUROPÉENNE EUROPÄISCHE NORM
EN 15948
March 2015 ICS 67.060 Supersedes EN 15948:2012English Version
Cereals - Determination of moisture and protein - Method using Near-Infrared-Spectroscopy in whole kernels
Céréales - Détermination de la teneur en eau et en protéines - Méthode utilisant la spectroscopie dans le proche infrarouge sur des grains entiers
Getreide - Bestimmung der Feuchte und des Proteins - Verfahren der Nahinfrarot-Spektroskopie bei ganzen Körnern This European Standard was approved by CEN on 5 January 2015.
CEN members are bound to comply with the CEN/CENELEC Internal Regulations which stipulate the conditions for giving this European Standard the status of a national standard without any alteration. Up-to-date lists and bibliographical references concerning such national standards may be obtained on application to the CEN-CENELEC Management Centre or to any CEN member.
This European Standard exists in three official versions (English, French, German). A version in any other language made by translation under the responsibility of a CEN member into its own language and notified to the CEN-CENELEC Management Centre has the same status as the official versions.
CEN members are the national standards bodies of Austria, Belgium, Bulgaria, Croatia, Cyprus, Czech Republic, Denmark, Estonia, Finland, Former Yugoslav Republic of Macedonia, France, Germany, Greece, Hungary, Iceland, Ireland, Italy, Latvia, Lithuania, Luxembourg, Malta, Netherlands, Norway, Poland, Portugal, Romania, Slovakia, Slovenia, Spain, Sweden, Switzerland, Turkey and United Kingdom.
EUROPEAN COMMITTEE FOR STANDARDIZATION
COMITÉ EUROPÉEN DE NORMALISATION EUROPÄISCHES KOMITEE FÜR NORMUNG
CEN-CENELEC Management Centre:
Avenue Marnix 17,
B-1000 Brussels © 2015 CEN All rights of exploitation in any form and by any means reserved worldwide for CEN national Members. Ref. No. EN 15948:2015 ESIST EN 15948:2015
EN 15948:2015 (E) 2 Contents Page Foreword .4 1 Scope .5 2 Normative references .5 3 Terms and definitions .5 4 Principle .5 5 Method of analysis.5 5.1 General .5 5.2 Near Infrared Instrument .6 5.3 Prediction models .6 5.4 Initial validation of the model .6 5.4.1 General .6 5.4.2 Initial validation sample set .6 5.4.3 Initial validation performances .7 5.5 Update of calibration model and validation of new model .7 6 Sampling .7 7 Procedure .7 7.1 Preparation of the test sample .7 7.2 Measurement .7 7.3 Local validation of the method .7 7.4 Periodical adjustment of the instrument .8 7.5 Checking instrument stability .8 7.6 Follow up of method performance .8 8 Calculation and expression of results .8 9 Accuracy and precision of the method .9 9.1 Accuracy .9 9.2 Precision .9 9.2.1 General .9 9.2.2 Repeatability .9 9.2.3 Reproducibility .9 9.2.4 Critical difference. 10 10 Test Report . 11 Annex A (informative)
Results of examples of interlaboratory test . 12 A.1 FOSS interlaboratory test . 12 A.2 PERTEN interlaboratory test . 17 A.3 CHOPIN Technologies interlaboratory test . 21 Annex B (informative)
Validation of ANN prediction model WB003034 . 29 B.1 ANN prediction model WB003034 with associated database . 29 B.2 Results of validation according to EN ISO 12099 . 29 B.3 Stability and robustness . 31 Annex C (informative)
Validation of Inframatic 9500 prediction models . 33 C.1 Perten Inframatic 9500 prediction models . 33 C.2 Results of validations according to EN ISO 12099 . 33 SIST EN 15948:2015
EN 15948:2015 (E) 3 Annex D (informative)
Validation of Infraneo prediction models . 39 D.1 CHOPIN Technologies Infraneo prediction models . 39 D.2 Results of validation according to EN ISO 12099 . 39 Bibliography . 45
SIST EN 15948:2015
EN 15948:2015 (E) 4 Foreword This document (EN 15948:2015) has been prepared by Technical Committee CEN/TC 338 “Cereal and cereal products”, the secretariat of which is held by AFNOR. This European Standard shall be given the status of a national standard, either by publication of an identical text or by endorsement, at the latest by October 2015, and conflicting national standards shall be withdrawn at the latest by October 2015. Attention is drawn to the possibility that some of the elements of this document may be the subject of patent rights. CEN [and/or CENELEC] shall not be held responsible for identifying any or all such patent rights. This document supersedes EN 15948:2012. The following modifications were made in this new edition: — Annexes have been enhanced with information on 2 other NIR instruments based on the results of the interlaboratory tests and the models of prediction. According to the CEN-CENELEC Internal Regulations, the national standards organizations of the following countries are bound to implement this European Standard: Austria, Belgium, Bulgaria, Croatia, Cyprus, Czech Republic, Denmark, Estonia, Finland, Former Yugoslav Republic of Macedonia, France, Germany, Greece, Hungary, Iceland, Ireland, Italy, Latvia, Lithuania, Luxembourg, Malta, Netherlands, Norway, Poland, Portugal, Romania, Slovakia, Slovenia, Spain, Sweden, Switzerland, Turkey and the United Kingdom. SIST EN 15948:2015
EN 15948:2015 (E) 5 1 Scope This European Standard defines a routine method for the determination of moisture and protein in whole kernels of barley and wheat using a near-infrared spectrophotometer in the constituent ranges: a) for wheat: 1) moisture content minimum range from 8 % to 22 %; 2) protein content minimum range from 7 % to 20 %. b) for barley: 1) moisture content minimum range from 8 % to 22 %; 2) protein content minimum range from 7 % to 16 %. This European Standard describes the modalities to be implemented by the supplier (5.3 and 5.4) and the user of the method. 2 Normative references The following documents, in whole or in part, are normatively referenced in this document and are indispensable for its application. For dated references, only the edition cited applies. For undated references, the latest edition of the referenced document (including any amendments) applies. EN ISO 12099:2010, Animal feeding stuffs, cereals and milled cereal products — Guidelines for the application of near infrared spectrometry (ISO 12099:2010) ISO 5725-2, Accuracy (trueness and precision) of measurement methods and results — Part 2: Basic method for the determination of repeatability and reproducibility of a standard measurement method 3 Terms and definitions For the purposes of this document, the terms and definitions given in EN ISO 12099:2010 apply. 4 Principle The method is based on Near-Infrared (NIR) spectroscopy, an indirect, correlative technique to predict the concentration of various constituents in organic samples. Linear or non-linear regression modelling is used to relate NIR spectra to moisture or protein concentrations determined by officially approved standard methods (e.g. artificial neural network - ANN, Partial Least Square Regression - PLS). 5 Method of analysis 5.1 General According to this document, the method of analysis is defined as the association between a NIR instrument and a model of prediction. SIST EN 15948:2015
EN 15948:2015 (E) 6 5.2 Near Infrared Instrument Based on diffuse reflectance or transmittance measurement covering the wavelength region of 700 nm to 2 500 nm or segments of this or at selected wavelengths. 5.3 Prediction models Each model for the prediction of protein and moisture contents in whole grain of wheat and barley is amongst others defined by: — the number of samples used for the calibration development; — the constituent ranges covered in the model for moisture and protein; — the temperature range of the samples; — the number and performance of involved reference labs; — the stability of the model i.e. by number of harvests covered; — the calibration file defined by its name and its IT name (for example CHECKSUM) insuring its integrity; — the seasonal, geographic and genetic variations covered. 5.4 Initial validation of the model 5.4.1 General Since NIR analysis is an indirect, correlative technique, the results shall be validated against chemical analysis reference methods. It is important that the reference methods used are officially approved such as the methods described in the standards previously cited (Clause 2). The purpose of validation is to determine the root mean square error of prediction which depends at the same time on the correlation, the bias and the slope. The root mean square error between chemical analysis methods and predictions shall be compared to calibration performance specifications and/or historical performance. 5.4.2 Initial validation sample set The initial validation of a calibration model shall be done in accordance with EN ISO 12099 using independent test sets of wheat and barley samples, originating from different countries and analyzed by the reference methods given in Clause 2. Requirements for the validation sample set are: — at least 200 samples coming from 10 countries (20 representative samples min/country) distributed homogeneously over the entire constituent range; — the part of the range without any reference sample shall not exceed 0,3 %; — different scans from one sample shall not be considered as different samples; — seasonal effects over at least a three year period, temperature effects, instrument variation and the variability of reference data shall be included in the set. SIST EN 15948:2015
EN 15948:2015 (E) 7 5.4.3 Initial validation performances The results of the initial validation shall at least fulfil the specifications given in Table 1. Table 1 — NIR performances for the determination of moisture and protein (see also Annex B)
Moisture Wheat and barley Protein Wheat Protein Barley Overall accuracy expressed as SEP as constituent % w/w 0,24 % 0,27 % 0,27 % Constituent concentration in the independent validation data set Min 8 % 7 % d.m. 7 % d.m. Max 22 % 20 % d.m. 16 % d.m. NOTE The minimum performance given in Table 1 includes the variation of reference data as documented by the number of reference labs involved, regional and genetic variations, the number of countries and crop species involved and the robustness over the last five years (see also Annex B). 5.5 Update of calibration model and validation of new model The prediction model in accordance with this standard shall be updated by the one issuing the calibration model to ensure inclusion of new climatic crop conditions and new varieties introduced on the market. These updates shall be made by keeping the original database with addition of the new samples as needed. The new prediction model shall be updated according to EN ISO 12099. Validation shall be made according to the initial validation (5.4) and include at least 20 new samples. 6 Sampling Sampling is not part of the method specified in this European Standard. A recommended sampling procedure is given in EN ISO 24333 [5]. It is important that the sample analyzed in routine is truly representative for the batch and has not been damaged or modified. 7 Procedure 7.1 Preparation of the test sample No specific sample preparation is required. 7.2 Measurement Follow the instructions of the instrument manufacturer. 7.3 Local validation of the method Before use, the method shall be validated on an independent test set that is representative of the sample population to be analyzed. For the determination of bias, at least 10 samples are needed; for the determination of Standard Error of Prediction (SEP, see EN ISO 12099:2010, 6.5) at least 20 samples are SIST EN 15948:2015
EN 15948:2015 (E) 8 needed. Validation shall be carried out for each sample type, constituent/ parameter and temperature (see EN ISO 12099:2010, 5.4). Bias or inherent systematic error, as described in EN ISO 12099:2010, Clause 6, is exhibited when the predicted results of a specific sample group or product show a mean offset value when compared to their reference values. This may occur with unique sample types. The bias (i.e. mean difference between the chemical analysis results and the predicted results) may or may not be statistically significant. Based on the procedure described in EN ISO 12099, a bias confidence limit may be calculated. When this limit is exceeded, a bias is implemented in the instrument software and the validation process repeated. Refer to the manufacturer instructions and to EN ISO 12099 for procedure. 7.4 Periodical adjustment of the instrument To ensure its accuracy, each instrument shall be checked at least annually, against the reference method, either directly or through a master instrument. The execution of this check shall be performed on samples covering a range as wide as possible, taking into account seasonal, geographical and genetic variations. The number of samples for the adjustment should be sufficient for the statistics used to check the performance. For the determination of the bias, at least 10 samples are needed, for the determination of standard error of prediction (SEP) and for the slope adjustment, at least 20 samples are needed. 7.5 Checking instrument stability See EN ISO 12099:2010, Clause 9. 7.6 Follow up of method performance Performance of the method shall be checked at least annually, against reference methods to secure the constant adequacy of the model with the requirements of this standard (see 5.4.2). This performance test shall be made on samples selected from the pool of analyzed samples. It may be necessary to resort to some sampling strategy to ensure a balanced sample distribution over the entire calibration range and to ensure that samples with a commercially important range are covered. At least 20 samples are needed (to expect a normal distribution of variance). For instruments operated in a network and adjusted against a master instrument, it is sufficient to run the performance check of the method of this last one. The adjustment (7.4) respecting the requirements of this clause may be used for the follow-up of the method performance. It is recommended to participate in an internationally accepted proficiency testing scheme (PTS) that includes NIRS predicted results and results generated by following the standards specified in Clause 2. 8 Calculation and expression of results The software of the instrument calculates the results for moisture and protein and displays them in % w/w (g/100 g) to two decimal places. If multiple measurements are made on the same sample, calculate the arithmetic mean. SIST EN 15948:2015
EN 15948:2015 (E) 9 Express final results to two decimal places. 9 Accuracy and precision of the method 9.1 Accuracy The accuracy of the prediction model is determined by validation in accordance with EN ISO 12099 and expressed by the Standard Error of Prediction (see EN ISO 12099:2010, Table 1). The Standard Error of Prediction (SEP) is an expression of the bias corrected average difference between predicted and reference values predicted by the model when applied to a set of samples not included in the derivation of the model. The values also include the uncertainty of reference results. The predicted results will not in more than 5 % of cases deviate more than 1,96 x SEP (as determined in the paragraph above) from the best estimate of the true value. NOTE As NIR is an indirect method, the typical standard deviation of reproducibility for the used reference methods are given here for comparison: — moisture (EN ISO 712) = 0,16 %; — protein (EN ISO 20483) = 0,20 %; — protein (EN ISO 5983-2) = 0,20 %; — protein (CEN ISO/TS 16634-2) = 0,21-0,26 %. 9.2 Precision 9.2.1 General The precision of the prediction model shall be determined from an interlaboratory test organized according to ISO 5725-2 and at least fulfil the performance criteria of repeatability and reproducibility given below. Details of an example of an interlaboratory test are summarized in Annex A. The precision data given below are derived from this example. Figure A.1 and Figure A.2 show that the repeatability and the reproducibility are independent of the concentration. The figures in Annex B show that the dispersion is identical over the validated range (Figure B.1 and Figure B.2). Therefore the model can be used in the whole validated range, even though the interlaboratory trial covered a smaller range. 9.2.2 Repeatability The absolute difference between two independent single test results, obtained using the same method on identical test material in the same laboratory by the same operator using the same equipment within a short interval of time will not in more than 5 % of cases be greater than the repeatability limit r (r=srx2,8) with: r protein = 0,42 % r moisture = 0,15 % 9.2.3 Reproducibility The absolute difference between two single test results, obtained using the same method on identical test material in different laboratories with different operators using different equipment, will not in more than 5 % of cases be greater than the reproducibility limit R (R=sRx2,8) with: SIST EN 15948:2015
EN 15948:2015 (E) 10 R protein = 0,45 % R moisture = 0,25 % 9.2.4 Critical difference 9.2.4.1 General When the difference between two averaged values obtained from two test results under repeatability or reproducibility conditions shall be assessed, the repeatability or reproducibility limit cannot be used, one shall use the Critical Difference (CD). 9.2.4.2 Comparison of two groups of measurements in one laboratory The critical difference (CD) between two averaged values obtained from two test results under repeatability conditions is equal to: 1112827719821222rrr,,,CDssSnn=+== where rs is the standard deviation of repeatability; 1n and 2n
are the numbers of test results corresponding to each of the averaged values; CDr (protein) = 0,30; CDr (moisture) = 0,11. 9.2.4.3 Comparison of two groups of measurements in two laboratories The critical difference (CD) between two averaged values obtained in two different laboratories from two test results under repeatability conditions is equal to: 22221211281280522RrRr,,,CDssssnn=−−−=− where rs is the standard deviation of repeatability; Rs is the standard deviation of reproducibility; 1n and 2n are the number of test results corresponding to each of the averaged values; CDR (protein) = 0,32; CDR (moisture) = 0,23. SIST EN 15948:2015
EN 15948:2015 (E) 11 10 Test Report The test report shall specify: a) all information necessary for the complete identification of the sample; b) the sampling method used (if known); c) the application model and instrument used with reference to this European Standard; d) all operating details not specified in this European Standard, or regarded as optional, together with details of any incidents which may have influenced the test result(s); e) the test result(s) obtained. SIST EN 15948:2015
EN 15948:2015 (E) 12 Annex A (informative)
Results of examples of interlaboratory test A.1 FOSS interlaboratory test An interlaboratory test, organized by the company FOSS Analytical AB (Sweden) in 2008, involving 20 participants from 12 countries was carried out on 6 wheat and 4 barley samples from the 2007 harvest, containing protein and moisture in various concentrations. The participants were the master labs of European grain networks. The grain networks did also assist in the collection of the samples (Table A.1). The results obtained were subjected to statistical analysis in accordance with ISO 5725-1 and ISO 5725-2 to calculate the precision data shown in Table A.2 to Table A.5. Table A.1 — Samples for the interlaboratory study Sample Description Country of origin B1 Spring barley (2-row, malting barley) UK B2 Spring barley (2-row, feed barley) Denmark B3 Spring barley (2-row, malting barley) Denmark B4 Winter barley (6-row, malting barley) France W1 Spring wheat (hard) Germany W2 Spring wheat (hard) France W3 Winter wheat (hard) UK W4 Spring wheat (soft) Germany W5 Winter wheat (hard) Italy W6 Durum wheat Italy SIST EN 15948:2015
EN 15948:2015 (E) 13 Table A.2 — Results of statistical analysis for the determination of the protein content in wheat by the ANN model WB003034 Sample WG 1 WG 2 WG 3 WG 4 WG 5 WG 6 Number of laboratories 20 20 20 20 20 20 Mean predicted protein content (% d.m.) 16,883 11,789 13,047 10,876 14,985 14,173 Repeatability standard deviation sr (% P) 0,159a 0,113a 0,099 0,106 0,087 0,109a Repeatability relative stand. dev. sr % 0,943 0,958 0,76 0,979 0,583 0,771 Repeatability limit r [ r = 2,8 x sr ], % 0,440 0,313 0,274 0,294 0,241 0,302 Reproducibility stand. dev. sR (% P) 0,159 0,113 0,107 0,143 0,125 0,109 Reproducibility relative stand. dev. sR % 0,943 0,958 0,819 1,317 0,834 0,771 Reproducibility limit R [R = 2,8 x sR], % 0,440 0,313 0,296 0,396 0,346 0,302 Best estimate of true protein value (%)b 16,88 11,75 13,08 10,86 14,93 14,50 Critical difference (n=2), reference methods 0,31 0,22 0,22 0,34 0,30 0,21 Deviation predicted - true value (%) 0,01 0,04 0,03 0,01 0,06 0,33 a sr > sR, sR set to be equal sr. b Average protein value (after elimination of outliers) generated by 17 Master labs of the European grain networks, using Kjeldahl (EN ISO 20483 or EN ISO 5983-2) and Dumas (CEN ISO/TS 16634-2) methods. Table A.3 — Results of statistical analysis for the determination of the protein content in barley by the ANN model WB003034 Sample B 1 B 2 B 3 B 4 Number of laboratories 20 20 20 20 Mean predicted protein content in % 10,964 13,05 11,15 12,772 Repeatability standard deviation sr (% P) 0,198a 0,169a 0,123 0,343a Repeatability relative stand. dev. sr % 1,804 1,292 1,105 2,682 Repeatability limit r [ r = 2,8 x sr ], % 0,548 0,468 0,341 0,95 Reproducibility stand. dev. sR (% P) 0,198 0,169 0,151 0,343 Reproducibility relative stand. dev. sR % 1,804 1,292 1,352 2,682 Reproducibility limit R [R = 2,8 x sR], % 0,548 0,468 0,418 0,950 Best estimate of true protein value (%)b 10,66 13,11 11,27 12,97 Critical difference (n=2), reference methods 0,39 0,33 0,34 0,67 Deviation predicted - true value (%) 0,30 -0,06 -0,12 -0,20 a sr > sR, sR set to be equal sr. b Average protein value (after elimination of outliers) generated by 17 Master labs of the European grain networks, using Kjeldahl (EN ISO 20483 or EN ISO 5983-2) and Dumas (CEN ISO/TS 16634-2) methods. SIST EN 15948:2015
EN 15948:2015 (E) 14 Table A.4 — Results of statistical analysis for the determination of the moisture content in wheat by ANN model WB003034 Sample WG 1 WG 2 WG 3 WG 4 WG 5 WG 6 Number of laboratories 20 20 20 20 20 20 Mean predicted moisture content in % 14,125 13,415 13,978 13,764 11,102 11,074 Repeatability standard dev sr (% H2O) 0,024 0,025 0,030 0,016 0,015 0,019 Repeatability relative stand. dev. sr % 0,170 0,187 0,215 0,116 0,138 0,167 Repeatability limit r [ r = 2,8 x sr ], % 0,066 0,069 0,083 0,044 0,042 0,053 Reproducibility stand. dev. sR (% H2O) 0,077 0,07 0,090 0,079 0,045 0,047 Reproducibility relative stand. dev. sR % 0,546 0,523 0,642 0,573 0,405 0,422 Reproducibility limit R [R = 2,8 x sR], % 0,213 0,194 0,249 0,219 0,125 0,130 Best estimate of true moisture value (%)a 14,24 13,61 14,08 13,93 11,35 11,23 Critical difference (n=2), reference methods 0,20 0,28 0,26 0,34 0,27 0,56 Deviation predicted - true value (%) -0,11 -0,20 -0,10 -0,17 -0,25 -0,15 a Average moisture value (after elimination of outliers) generated by 17 Master labs of the European grain networks, using EN ISO 712 method. Table A.5 — Results of statistical analysis for the determination of the moisture content in barley by ANN model WB003034 Sample B1 B2 B3 B4 Number of laboratories 20 20 20 20 Mean predicted moisture content in % 12,417 13,731 14,299 12,986 Repeatability standard dev sr (% H2O) 0,103 0,053 0,054 0,213 Repeatability relative stand. dev. sr % 0,827 0,384 0,377 1,639 Repeatability limit r [ r = 2,8 x sr ], % 0,285 0,147 0,150 0,590 Reproducibility stand. dev. sR (% H2O) 0,111 0,071 0,087 0,238 Reproducibility relative stand. dev. sR % 0,896 0,517 0,611 1,836 Reproducibility limit R [R = 2,8 x sR], % 0,307 0,197 0,241 0,659 Best estimate of true moisture value (%)a 12,51 13,79 14,37 12,83 Critical difference (n=2), reference methods 0,14 0,29 0,30 0,16 Deviation predicted - true value (%) -0,09 -0,06 -
...
SLOVENSKI STANDARD
kSIST FprEN 15948:2014
01-september-2014
äLWR'RORþDQMHYODJHLQEHOMDNRYLQ0HWRGD]XSRUDEREOLåQMHLQIUDUGHþH
VSHNWURVNRSLMHYFHOLK]UQLK
Cereals - Determination of moisture and protein - Method using Near-Infrared-
Spectroscopy in whole kernels
Getreide - Bestimmung der Feuchte und des Proteins - Verfahren der Nahinfrarot-
Spektroskopie bei ganzen Körnern
Céréales - Détermination de la teneur en eau et en protéines - Méthode utilisant la
spectroscopie dans le proche infrarouge sur des grains entiers
Ta slovenski standard je istoveten z: FprEN 15948
ICS:
67.060 äLWDVWURþQLFHLQSURL]YRGLL] Cereals, pulses and derived
QMLK products
kSIST FprEN 15948:2014 en,fr,de
2003-01.Slovenski inštitut za standardizacijo. Razmnoževanje celote ali delov tega standarda ni dovoljeno.
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kSIST FprEN 15948:2014
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kSIST FprEN 15948:2014
EUROPEAN STANDARD
FINAL DRAFT
FprEN 15948
NORME EUROPÉENNE
EUROPÄISCHE NORM
July 2014
ICS 67.060 Will supersede EN 15948:2012
English Version
Cereals - Determination of moisture and protein - Method using
Near-Infrared-Spectroscopy in whole kernels
Céréales - Détermination de la teneur en eau et en Getreide - Bestimmung der Feuchte und des Proteins -
protéines - Méthode utilisant la spectroscopie dans le Verfahren der Nahinfrarot-Spektroskopie bei ganzen
proche infrarouge sur des grains entiers Körnern
This draft European Standard is submitted to CEN members for unique acceptance procedure. It has been drawn up by the Technical
Committee CEN/TC 338.
If this draft becomes a European Standard, CEN members are bound to comply with the CEN/CENELEC Internal Regulations which
stipulate the conditions for giving this European Standard the status of a national standard without any alteration.
This draft European Standard was established by CEN in three official versions (English, French, German). A version in any other language
made by translation under the responsibility of a CEN member into its own language and notified to the CEN-CENELEC Management
Centre has the same status as the official versions.
CEN members are the national standards bodies of Austria, Belgium, Bulgaria, Croatia, Cyprus, Czech Republic, Denmark, Estonia,
Finland, Former Yugoslav Republic of Macedonia, France, Germany, Greece, Hungary, Iceland, Ireland, Italy, Latvia, Lithuania,
Luxembourg, Malta, Netherlands, Norway, Poland, Portugal, Romania, Slovakia, Slovenia, Spain, Sweden, Switzerland, Turkey and United
Kingdom.
Recipients of this draft are invited to submit, with their comments, notification of any relevant patent rights of which they are aware and to
provide supporting documentation.
Warning : This document is not a European Standard. It is distributed for review and comments. It is subject to change without notice and
shall not be referred to as a European Standard.
EUROPEAN COMMITTEE FOR STANDARDIZATION
COMITÉ EUROPÉEN DE NORMALISATION
EUROPÄISCHES KOMITEE FÜR NORMUNG
CEN-CENELEC Management Centre: Avenue Marnix 17, B-1000 Brussels
© 2014 CEN All rights of exploitation in any form and by any means reserved Ref. No. FprEN 15948:2014 E
worldwide for CEN national Members.
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kSIST FprEN 15948:2014
FprEN 15948:2014 (E)
Contents Page
Foreword .3
1 Scope .4
2 Normative references .4
3 Terms and definitions .4
4 Principle .4
5 Method of analysis.4
6 Sampling .6
7 Procedure .6
8 Calculation and expression of results .7
9 Accuracy and precision of the method .8
10 Test Report .9
Annex A (informative) Results of examples of interlaboratory test . 11
A.1 FOSS interlaboratory test . 11
A.2 PERTEN interlaboratory test . 15
A.3 CHOPIN Technologies interlaboratory test . 19
Annex B (informative) Validation of ANN prediction model WB003034 . 26
B.1 ANN prediction model WB003034 with associated database . 26
B.2 Results of validation according to EN ISO 12099 . 26
B.3 Stability and robustness . 28
Annex C (informative) Validation of Inframatic 9500 prediction models . 30
C.1 Perten Inframatic 9500 prediction models . 30
C.2 Results of validations according to EN ISO 12099 . 30
Annex D (informative) Validation of Infraneo prediction models . 36
D.1 CHOPIN Technologies Infraneo prediction models . 36
D.2 Results of validation according to EN ISO 12099 . 36
Bibliography . 41
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Foreword
This document (FprEN 15948:2014) has been prepared by Technical Committee CEN/TC 338 “Cereal and
cereal products”, the secretariat of which is held by AFNOR.
This document will supersede EN 15948:2012.
This document is currently submitted to the Unique Acceptance Procedure.
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1 Scope
This European Standard defines a routine method for the determination of moisture and protein in whole
kernels of barley and wheat using a near-infrared spectrophotometer in the constituent ranges:
a) for wheat:
1) moisture content minimum range from 8 % to 22 %;
2) protein content minimum range from 7 % to 20 %.
b) for barley:
1) moisture content minimum range from 8 % to 22 %;
2) protein content minimum range from 7 % to 16 %.
This European Standard describes the modalities to be implemented by the supplier (5.3 and 5.4) and the
user of the method.
2 Normative references
The following documents, in whole or in part, are normatively referenced in this document and are
indispensable for its application. For dated references, only the edition cited applies. For undated references,
the latest edition of the referenced document (including any amendments) applies.
EN ISO 12099:2010, Animal feeding stuffs, cereals and milled cereal products - Guidelines for the application
of near infrared spectrometry (ISO 12099:2010)
ISO 5725-2, Accuracy (trueness and precision) of measurement methods and results - Part 2: Basic method
for the determination of repeatability and reproducibility of a standard measurement method
3 Terms and definitions
For the purposes of this document, the terms and definitions given in EN ISO 12099:2010 apply.
4 Principle
The method is based on Near-Infrared (NIR) spectroscopy, an indirect, correlative technique to predict the
concentration of various constituents in organic samples. Linear or nonlinear regression modelling is used to
relate NIR spectra to moisture or protein concentrations determined by officially approved standard methods
(e.g. artificial neural network - ANN, Partial Least Square Regression - PLS).
5 Method of analysis
5.1 General
According to this document, the method of analysis is defined as the association between a NIR instrument
and a model of prediction.
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5.2 Near Infrared Instrument
Based on diffuse reflectance or transmittance measurement covering the wavelength region of 700 nm–
2500 nm or segments of this or at selected wavelengths.
5.3 Prediction models
Each model for the prediction of protein and moisture contents in whole grain of wheat and barley is amongst
others defined by:
— the number of samples used for the calibration development;
— the constituent ranges covered in the model for moisture and protein;
— the temperature range of the samples;
— the number and performance of involved reference labs;
— the stability of the model i.e. by number of harvests covered;
— the calibration file defined by its name and its IT name (for example CHECKSUM) insuring its integrity;
— the seasonal, geographic and genetic variations covered.
5.4 Initial validation of the model
5.4.1 General
Since NIR analysis is an indirect, correlative technique, the results shall be validated against chemical
analysis reference methods. It is important that the reference methods used are officially approved such as
the methods described in the EN ISO standards previously cited (Clause 2). The purpose of validation is to
determine the root mean square error of prediction which depends at the same time on the correlation, the
bias and the slope.
The root mean square error between chemical analysis methods and predictions shall be compared to
calibration performance specifications and/or historical performance.
5.4.2 Initial validation sample set
The initial validation of a calibration model shall be done in accordance with EN ISO 12099 using independent
test sets of wheat and barley samples, originating from different countries and analysed by the reference
methods given in Clause 2.
Requirements for the validation sample set are:
— at least 200 samples coming from 10 countries (20 representative samples min/country) distributed
homogeneously over the entire constituent range;
— the part of the range without any reference sample shall not exceed 0,3 %;
— different scans from one sample shall not be considered as different samples;
— seasonal effects over at least a three year period, temperature effects, instrument variation and the
variability of reference data shall be included in the set.
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5.4.3 Initial validation performances
The results of the initial validation shall at least fulfil the specifications given in Table 1.
Table 1 — NIR performances for the determination of moisture and protein (see also Annex B)
Moisture Protein Protein
Wheat and Wheat Barley
barley
Overall accuracy expressed as SEP as 0,24 % 0,27 % 0,27 %
constituent % w/w
Constituent concentration in Min 8,0 % 7 % d.m. 7 % d.m.
the independent validation
Max 22 % 20 % d.m. 16 % d.m.
data set
NOTE The minimum performance given in Table 1 includes the variation of reference data as documented by the
number of reference labs involved, regional and genetic variations, the number of countries and crop species involved and
the robustness over the last five years (see also Annex B).
5.5 Update of calibration model and validation of new model
The prediction model in accordance with this standard shall be updated by the one issuing the calibration
model to ensure inclusion of new climatic crop conditions and new varieties introduced on the market. These
updates shall be made by keeping the original database with addition of the new samples as needed.
The new prediction model shall be updated according to EN ISO 12099.
Validation shall be made according to the initial validation (5.3) and include at least 20 new samples.
6 Sampling
Sampling is not part of the method specified in this European Standard. A recommended sampling procedure
is given in EN ISO 24333 [2].
It is important that the sample analysed in routine is truly representative for the batch and has not been
damaged or modified.
7 Procedure
7.1 Preparation of the test sample
No specific sample preparation is required.
7.2 Measurement
Follow the instructions of the instrument manufacturer.
7.3 Local validation of the method
Before use, the method shall be validated on an independent test set that is representative of the sample
population to be analysed. For the determination of bias, at least 10 samples are needed; for the
determination of Standard Error of Prediction (SEP, see EN ISO 12099:2010, Clause 6.5) at least 20 samples
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are needed. Validation shall be carried out for each sample type, constituent/ parameter and temperature (see
EN ISO 12099:2010, Clause 5.4).
Bias or inherent systematic error, as described in EN ISO 12099:2010, (Clause 6), is exhibited when the
predicted results of a specific sample group or product show a mean offset value when compared to their
reference values. This may occur with unique sample types.
The bias (i.e. mean difference between the chemical analysis results and the predicted results) may or may
not be statistically significant. Based on the procedure described in EN ISO 12099, a bias confidence limit can
be calculated.
When this limit is exceeded, a bias is implemented in the instrument software and the validation process
repeated. Refer to the manufacturer instructions and to EN ISO 12099 for procedure.
7.4 Periodical adjustment of the instrument
To ensure its accuracy, each instrument shall be checked at least annually, against the reference method,
either directly or through a master instrument.
The execution of this check shall be performed on samples covering a range as wide as possible, taking into
account seasonal, geographic and genetic variations.
The number of samples for the adjustment should be sufficient for the statistics used to check the
performance. For the determination of the bias, at least 10 samples are needed, for the determination of
standard error of prediction (SEP) and for the slope adjustment, at least 20 samples are needed.
7.5 Checking instrument stability
See Clause 9 of EN ISO 12099:2010.
7.6 Follow up of method performance
Performance of the method shall be checked at least annually, against reference methods to secure the
constant adequacy of the model with the requirements of this standard (see 5.4.2).
This performance test shall be made on samples selected from the pool of analysed samples. It may be
necessary to resort to some sampling strategy to ensure a balanced sample distribution over the entire
calibration range and to ensure that samples with a commercially important range are covered. At least
20 samples are needed (to expect a normal distribution of variance).
For instruments operated in a network and adjusted against a master instrument, it is sufficient to run the
performance check of the method of this last one.
The adjustment (7.4) respecting the requirements of this clause may be used for the follow-up of the method
performance.
It is recommended to participate in an internationally accepted proficiency testing scheme (PTS) that includes
NIRS predicted results and results generated by following the standards specified in Clause 2.
8 Calculation and expression of results
The software of the instrument calculates the results for moisture and protein and displays them in % w/w
(g/100 g) to two decimal places.
If multiple measurements are made on the same sample, calculate the arithmetic mean.
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Express final results to two decimal places.
9 Accuracy and precision of the method
9.1 Accuracy
The accuracy of the prediction model is determined by validation in accordance with EN ISO 12099:2010 and
expressed by the Standard Error of Prediction (see Table 1). The Standard Error of Prediction (SEP) is an
expression of the bias corrected average difference between predicted and reference values predicted by the
model when applied to a set of samples not included in the derivation of the model. The values also include
the uncertainty of reference results.
The predicted results will not in more than 5 % of cases deviate more than 1,96 x SEP (as determined in the
above paragraph) from the best estimate of the true value.
NOTE As NIR is an indirect method, the typical standard deviation of reproducibility for the used reference methods
are given here for comparison:
— Moisture (EN ISO 712) = 0,16 %;
— Protein (EN ISO 20483) = 0,20 %;
— Protein (EN ISO 5983-2) = 0,20 %;
— Protein (CEN ISO/TS 16634-2)= 0,21-0,26 %.
9.2 Precision
9.2.1 General
The precision of the prediction model shall be determined from an interlaboratory test organized according to
ISO 5725-2 and at least fulfil the performance criteria of repeatability and reproducibility given below.
Details of an example of an interlaboratory test are summarized in Annex A. The precision data given below
are derived from this example.
Figure A.1 and Figure A.2 show that the repeatability and the reproducibility are independent of the
concentration. The figures in Annex B show that the dispersion is identical over the validated range
(Figure B.1 and Figure B.2). The model can therefore be used in the whole validated range, even though the
interlaboratory trial covered a smaller range.
9.2.2 Repeatability
The absolute difference between two independent single test results, obtained using the same method on
identical test material in the same laboratory by the same operator using the same equipment within a short
interval of time will not in more than 5 % of cases be greater than the repeatability limit r (r=s x2,8) with:
r
r = 0,42 %
protein
r = 0,15 %
moisture
9.2.3 Reproducibility
The absolute difference between two single test results, obtained using the same method on identical test
material in different laboratories with different operators using different equipment, will not in more than 5 % of
cases be greater than the reproducibility limit R (R=s x2,8) with:
R
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R = 0,45 %
protein
R = 0,25 %
moisture
9.2.4 Critical difference
9.2.4.1 General
When the difference between two averaged values obtained from two test results under repeatability or
reproducibility conditions is to be assessed, the repeatability or reproducibility limit cannot be used, one shall
use the Critical Difference (CD).
9.2.4.2 Comparison of two groups of measurements in one laboratory
The critical difference (CD) between two averaged values obtained from two test results under repeatability
conditions is equal to:
1 1 1
CD= 2,8s + = 2,77s =1,98S
r r r
2n1 2n2 2
where
is the standard deviation of repeatability;
s
r
are the number of test results corresponding to each of the averaged
n1 and n2
values;
= 0,30;
CD (protein)
r
= 0,11.
CD (moisture)
r
9.2.4.3 Comparison of two groups of measurements in two laboratories
The critical difference (CD) between two averaged values obtained in two different laboratories from two test
results under repeatability conditions is equal to:
1 1
2 2 2 2
CD= 2,8 s −s1− − = 2,8 s − 0,5s
R r R r
2n 2n
1 2
where
is the standard deviation of repeatability;
s
r
is the standard deviation of reproducibility;
s
R
are the number of test results corresponding to each of the averaged values;
n and n
1 2
= 0,32;
CD (protein)
R
= 0,23.
CD (moisture)
R
10 Test Report
The test report shall specify:
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a) all information necessary for the complete identification of the sample;
b) the sampling method used (if known);
c) the application model and instrument used with reference to this European Standard;
d) all operating details not specified in this European Standard, or regarded as optional, together with details
of any incidents which may have influenced the test result(s);
e) the test result(s) obtained.
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Annex A
(informative)
Results of examples of interlaboratory test
A.1 FOSS interlaboratory test
An interlaboratory test, organized by the company FOSS Analytical AB (Sweden) in 2008, involving
20 participants from 12 countries was carried out on 6 wheat and 4 barley samples from the 2007 harvest,
containing protein and moisture in various concentrations. The participants were the master labs of European
grain networks. The grain networks did also assist in the collection of the samples (Table A.1).
The results obtained were subjected to statistical analysis in accordance with ISO 5725-1 and ISO 5725-2 to
calculate the precision data shown in Table A.2 to Table A.5.
Table A.1 — Samples for the interlaboratory study
Sample Description Country of origin
B1 Spring barley (2-row, malting barley) UK
B2 Spring barley (2-row, feed barley) Denmark
B3 Spring barley (2-row, malting barley) Denmark
B4 Winter barley (6-row, malting barley) France
W1 Spring wheat (hard) Germany
W2 Spring wheat (hard) France
W3 Winter wheat (hard) UK
W4 Spring wheat (soft) Germany
W5 Winter wheat (hard) Italy
W6 Durum wheat Italy
Table A.2 — Results of statistical analysis for the determination of the protein content in wheat by the
ANN model WB003034
Sample WG 1 WG 2 WG 3 WG 4 WG 5 WG 6
Number of laboratories 20 20 20 20 20 20
Mean predicted protein content (% d.m.) 16,883 11,789 13,047 10,876 14,985 14,173
a a a
Repeatability standard deviation s (% P) 0,099 0,106 0,087
r 0,159 0,113 0,109
Repeatability relative stand. dev. s % 0,943 0,958 0,76 0,979 0,583 0,771
r
Repeatability limit r [ r = 2,8 x s ], % 0,440 0,313 0,274 0,294 0,241 0,302
r
Reproducibility stand. dev. s (% P) 0,159 0,113 0,107 0,143 0,125 0,109
R
Reproducibility relative stand. dev. s % 0,943 0,958 0,819 1,317 0,834 0,771
R
Reproducibility limit R [R = 2,8 x s ], % 0,440 0,313 0,296 0,396 0,346 0,302
R
b 16,88 11,75 13,08 10,86 14,93 14,50
Best estimate of true protein value (%)
Critical difference (n=2), reference 0,31 0,22 0,22 0,34 0,30 0,21
methods
Deviation predicted - true value (%) 0,01 0,04 0,03 0,01 0,06 0,33
a
s > s , s set to be equal s .
r R R r
b
Average protein value (after elimination of outliers) generated by 17 Master labs of the European grain networks,
using Kjeldahl (EN ISO 20483 or EN ISO 5983-2) and Dumas (CEN ISO/TS 16634-2) methods.
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Table A.3 — Results of statistical analysis for the determination of the protein content in barley by the
ANN model WB003034
Sample B 1 B 2 B 3 B 4
Number of laboratories 20 20 20 20
Mean predicted protein content in % 10,964 13,05 11,15 12,772
Repeatability standard deviation s (% P) a a a
0,123
r 0,198 0,169 0,343
Repeatability relative stand. dev. s %
1,804 1,292 1,105 2,682
r
Repeatability limit r [ r = 2,8 x sr ], % 0,548 0,468 0,341 0,95
Reproducibility stand. dev. s (% P) 0,198 0,169 0,151 0,343
R
Reproducibility relative stand. dev. s % 1,804 1,292 1,352 2,682
R
Reproducibility limit R [R = 2,8 x s ], % 0,548 0,468 0,418 0,950
R
b
10,66 13,11 11,27 12,97
Best estimate of true protein value (%)
Critical difference (n=2), reference methods 0,39 0,33 0,34 0,67
Deviation predicted - true value (%) 0,30 -0,06 -0,12 -0,20
a
s > s , s set to be equal s .
r R R r
b
Average protein value (after elimination of outliers) generated by 17 Master labs of the European grain networks,
using Kjeldahl (EN ISO 20483 or EN ISO 5983-2) and Dumas (CEN ISO/TS 16634-2) methods.
Table A.4 — Results of statistical analysis for the determination of the moisture content in wheat by
ANN model WB003034
Sample WG 1 WG 2 WG 3 WG 4 WG 5 WG 6
Number of laboratories 20 20 20 20 20 20
Mean predicted moisture content in % 14,125 13,415 13,978 13,764 11,102 11,074
Repeatability standard dev s (% H O)
0,024 0,025 0,030 0,016 0,015 0,019
r 2
Repeatability relative stand. dev. s %
0,170 0,187 0,215 0,116 0,138 0,167
r
Repeatability limit r [ r = 2,8 x s ], %
0,066 0,069 0,083 0,044 0,042 0,053
r
Reproducibility stand. dev. s (% H O)
R 0,077 0,07 0,090 0,079 0,045 0,047
2
Reproducibility relative stand. dev. s % 0,546 0,523 0,642 0,573 0,405 0,422
R
Reproducibility limit R [R = 2,8 x s ], % 0,213 0,194 0,249 0,219 0,125 0,130
R
a
14,24 13,61 14,08 13,93 11,35 11,23
Best estimate of true moisture value (%)
Critical difference (n=2), reference
0,20 0,28 0,26 0,34 0,27 0,56
methods
Deviation predicted - true value (%) -0,11 -0,20 -0,10 -0,17 -0,25 -0,15
a
Average moisture value (after elimination of outliers) generated by 17 Master labs of the European grain networks,
using EN ISO 712 method.
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Table A.5 — Results of statistical analysis for the determination of the moisture content in barley by
ANN model WB003034
Sample B1 B2 B3 B4
Number of laboratories 20 20 20 20
Mean predicted moisture content in % 12,417 13,731 14,299 12,986
Repeatability standard dev s (% H O)
0,103 0,053 0,054 0,213
r 2
Repeatability relative stand. dev. s %
0,827 0,384 0,377 1,639
r
Repeatability limit r [ r = 2,8 x s ], %
0,285 0,147 0,150 0,590
r
Reproducibility stand. dev. s (% H O)
R
0,111 0,071 0,087 0,238
2
Reproducibility relative stand. dev. s % 0,896 0,517 0,611 1,836
R
Reproducibility limit R [R = 2,8 x s ], % 0,307 0,197 0,241 0,659
R
a
12,51 13,79 14,37 12,83
Best estimate of true moisture value (%)
Critical difference (n=2), reference methods 0,14 0,29 0,30 0,16
Deviation predicted - true value (%) -0,09 -0,06 -0,07 0,16
a
Average moisture value (after elimination of outliers) generated by 17 Master labs of the European grain networks,
using EN ISO 712 method.
Key
X % protein (dm)
Y s%
1 ◆ s
r
2 ◼ S
R
3 Linear (s )
r
Linear (s )
4
R
Figure A.1 — Standard deviations for the repeatability, s , and reproducibility, s , as a function of the
r R
protein content
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Key
X % moisture
Y s%
1 ◆ s
r
◼ S
2 R
Linear (s )
3
r
Linear (s )
4
R
Figure A.2 — Standard deviations for the repeatability, s , and reproducibility, s , as a function of the
r R
moisture content
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A.2 PERTEN interlaboratory test
An interlaboratory test, organized by the company Perten Instruments AB (Sweden) in 2013, involving
8 participants from 6 countries was carried out on 6 wheat and 4 barley samples from the 2012 harvest,
containing protein and moisture in various concentrations. The participants were the master or reference labs
of European grain networks. The grain networks did also assist in the collection of the samples (Table A.6).
The results obtained were subjected to statistical analysis in accordance with ISO 5725-2 to calculate the
precision data shown in Table A.7 to Table A.10.
Table A.6 — Samples for the interlaboratory study
Sample Description Country of origin
Winter barley France
B1
Germany
B2 Spring barley (blind duplicate to B4)
Sweden
B3 Spring barley
Spring barley (blind duplicate to B2) Germany
B4
Sweden
W1 Winter wheat
United Kingdom
W2 Wheat
Winter wheat Germany
W3
Germany
W4 Winter wheat
France
D1 Durum wheat
Durum wheat Italy
D2
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Table A.7 — Results of statistical analysis for the determination of the protein content in wheat by
Inframatic 9500 Wheat Calibrations Version 5, March 19, 2013
Sample W 1 W 2 W 3 W 4 D 1 D 2
Number of laboratories 8 8 8 8 8 8
Mean predicted protein content (% d.m.) 11,17 11,70 14,88 1
...
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