SIST EN ISO 12099:2010
(Main)Animal feeding stuffs, cereals and milled cereal products - Guidelines for the application of near infrared spectrometry (ISO 12099:2010)
Animal feeding stuffs, cereals and milled cereal products - Guidelines for the application of near infrared spectrometry (ISO 12099:2010)
This International Standard gives guidelines for the determination by near infrared spectroscopy of constituents such as moisture, fat, protein, starch, and crude fibre as well as parameters such as digestibility in animal feeding stuffs, cereals and milled cereal products. The determinations are based on spectrometric measurement in the near infrared spectral region.
Futtermittel, Getreide und gemahlene Getreideerzeugnisse - Anleitung für die Anwendung von Nahinfrarot-Spektrometrie (ISO 12099:2010)
Diese Internationale Norm stellt eine Anleitung für die Bestimmung von Bestandteilen, wie z. B. Feuchte, Fett, Protein, Stärke und Rohfaser und von Parametern, wie z. B. Verdaubarkeit des Futtermittels, von Getreide und gemahlenen Getreideerzeugnissen mit Nahinfrarot-Spektroskopie bereit.
Die Bestimmungen basieren auf einer spektrometrischen Messung im Nahinfrarotbereich.
Aliments des animaux, céréales et produits de mouture des céréales - Lignes directrices pour l'application de la spectrométrie dans le proche infrarouge (ISO 12099:2010)
L'ISO 12099:2010 fournit des lignes directrices pour la détermination par spectrométrie dans le proche infrarouge de constituants tels que l'eau, les matières grasses, les protéines, l'amidon et la cellulose brute, ainsi que des paramètres tels que la digestibilité des aliments pour animaux, des céréales et des produits céréaliers moulus.
Les déterminations sont basées sur des mesurages spectrométriques dans le domaine du proche infrarouge.
Krma, žito in mlevski proizvodi - Smernice za uporabo bližnje infrardeče spektrometrije (ISO 12099:2010)
Ta mednarodni standard podaja smernice za določevanje bližnje infrardeče spektrometrije za sestavne dele, kot so vlaga, maščoba, proteini, škrob in surova vlakna ter za določevanje parametrov kot so prebavljivost krme, žita in mlevskih proizvodov. Določevanje je osnovano na spektrometrijski meritvi v bližnjem infrardečem spektralnem območju.
General Information
Relations
Standards Content (Sample)
SLOVENSKI STANDARD
SIST EN ISO 12099:2010
01-november-2010
.UPDåLWRLQPOHYVNLSURL]YRGL6PHUQLFH]DXSRUDEREOLåQMHLQIUDUGHþH
VSHNWURPHWULMH ,62
Animal feeding stuffs, cereals and milled cereal products - Guidelines for the application
of near infrared spectrometry (ISO 12099:2010)
Futtermittel, Getreide und gemahlene Getreideerzeugnisse - Anleitung für die
Anwendung von Nahinfrarot-Spektrometrie (ISO 12099:2010)
Aliments des animaux, céréales et produits de mouture des céréales - Lignes directrices
pour l'application de la spectrométrie dans le proche infrarouge (ISO 12099:2010)
Ta slovenski standard je istoveten z: EN ISO 12099:2010
ICS:
65.120 Krmila Animal feeding stuffs
67.060 äLWDVWURþQLFHLQSURL]YRGLL] Cereals, pulses and derived
QMLK products
SIST EN ISO 12099:2010 en
2003-01.Slovenski inštitut za standardizacijo. Razmnoževanje celote ali delov tega standarda ni dovoljeno.
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SIST EN ISO 12099:2010
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SIST EN ISO 12099:2010
EUROPEAN STANDARD
EN ISO 12099
NORME EUROPÉENNE
EUROPÄISCHE NORM
June 2010
ICS 65.120
English Version
Animal feeding stuffs, cereals and milled cereal products -
Guidelines for the application of near infrared spectrometry (ISO
12099:2010)
Aliments des animaux, céréales et produits de mouture des Futtermittel, Getreide und gemahlene Getreideerzeugnisse
céréales - Lignes directrices pour l'application de la - Anleitung für die Anwendung von Nahinfrarot-
spectrométrie dans le proche infrarouge (ISO 12099:2010) Spektrometrie (ISO 12099:2010)
This European Standard was approved by CEN on 12 June 2010.
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 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 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, France, Germany, Greece, Hungary, Iceland, Ireland, Italy, Latvia, Lithuania, Luxembourg, Malta, Netherlands, Norway, Poland,
Portugal, Romania, Slovakia, Slovenia, Spain, Sweden, Switzerland and United Kingdom.
EUROPEAN COMMITTEE FOR STANDARDIZATION
COMITÉ EUROPÉEN DE NORMALISATION
EUROPÄISCHES KOMITEE FÜR NORMUNG
Management Centre: Avenue Marnix 17, B-1000 Brussels
© 2010 CEN All rights of exploitation in any form and by any means reserved Ref. No. EN ISO 12099:2010: E
worldwide for CEN national Members.
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SIST EN ISO 12099:2010
EN ISO 12099:2010 (E)
Contents Page
Foreword .3
2
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SIST EN ISO 12099:2010
EN ISO 12099:2010 (E)
Foreword
This document (EN ISO 12099:2010) has been prepared by Technical Committee CEN/TC 327 “Animal
feeding stuffs - Methods of sampling and analysis” the secretariat of which is held by NEN, in collaboration
with Technical Committee ISO/TC 34 "Food products".
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 December 2010, and conflicting national standards shall be withdrawn
at the latest by December 2010.
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.
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, France, Germany, Greece, Hungary, Iceland, Ireland, Italy, Latvia,
Lithuania, Luxembourg, Malta, Netherlands, Norway, Poland, Portugal, Romania, Slovakia, Slovenia, Spain,
Sweden, Switzerland and the United Kingdom.
3
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SIST EN ISO 12099:2010
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SIST EN ISO 12099:2010
INTERNATIONAL ISO
STANDARD 12099
First edition
2010-06-15
Animal feeding stuffs, cereals and milled
cereal products — Guidelines for the
application of near infrared spectrometry
Aliments des animaux, céréales et produits de mouture des céréales —
Lignes directrices pour l'application de la spectrométrie dans le proche
infrarouge
Reference number
ISO 12099:2010(E)
©
ISO 2010
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SIST EN ISO 12099:2010
ISO 12099:2010(E)
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ii © ISO 2010 – All rights reserved
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SIST EN ISO 12099:2010
ISO 12099:2010(E)
Contents Page
Foreword .iv
Introduction.v
1 Scope.1
2 Terms and definitions .1
3 Principle.2
4 Apparatus.2
5 Calibration and initial validation .2
6 Statistics for performance measurement .4
7 Sampling.11
8 Procedure.11
9 Checking instrument stability .12
10 Running performance check of calibration .12
11 Precision and accuracy .14
12 Test report.14
Annex A (informative) Guidelines for specific NIR standards.15
Annex B (informative) Examples of figures.16
Annex C (informative) Supplementary terms and definitions .22
Bibliography.29
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SIST EN ISO 12099:2010
ISO 12099:2010(E)
Foreword
ISO (the International Organization for Standardization) is a worldwide federation of national standards bodies
(ISO member bodies). The work of preparing International Standards is normally carried out through ISO
technical committees. Each member body interested in a subject for which a technical committee has been
established has the right to be represented on that committee. International organizations, governmental and
non-governmental, in liaison with ISO, also take part in the work. ISO collaborates closely with the
International Electrotechnical Commission (IEC) on all matters of electrotechnical standardization.
International Standards are drafted in accordance with the rules given in the ISO/IEC Directives, Part 2.
The main task of technical committees is to prepare International Standards. Draft International Standards
adopted by the technical committees are circulated to the member bodies for voting. Publication as an
International Standard requires approval by at least 75 % of the member bodies casting a vote.
Attention is drawn to the possibility that some of the elements of this document may be the subject of patent
rights. ISO shall not be held responsible for identifying any or all such patent rights.
ISO 12099 was prepared by the European Committee for Standardization (CEN) Technical committee TC 327,
Animal feeding stuffs — Methods of sampling and analysis, in collaboration with ISO Technical Committee
TC 34, Food products, Subcommittee SC 10, Animal feeding stuffs, in accordance with the Agreement on
technical cooperation between ISO and CEN (Vienna agreement).
iv © ISO 2010 – All rights reserved
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SIST EN ISO 12099:2010
ISO 12099:2010(E)
Introduction
[15]
This International Standard has been drafted using, as a basis, ISO 21543⎪IDF 201 , prepared by Technical
Committee ISO/TC 34, Food products, Subcommittee SC 5, Milk and milk products, and the International
Dairy Federation (IDF).
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SIST EN ISO 12099:2010
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SIST EN ISO 12099:2010
INTERNATIONAL STANDARD ISO 12099:2010(E)
Animal feeding stuffs, cereals and milled cereal products —
Guidelines for the application of near infrared spectrometry
1 Scope
This International Standard gives guidelines for the determination by near infrared spectroscopy of
constituents such as moisture, fat, protein, starch, and crude fibre as well as parameters such as digestibility
in animal feeding stuffs, cereals and milled cereal products.
The determinations are based on spectrometric measurement in the near infrared spectral region.
2 Terms and definitions
For the purposes of this International Standard, the following terms and definitions apply.
2.1
near infrared instrument
NIR instrument
apparatus which, when used under specified conditions, predicts constituent contents (2.3) and
technological parameters (2.4) in a matrix through relationships to absorptions in the near infrared range
NOTE In the context of this International Standard, the matrices are animal feeding stuffs, cereals and milled cereal
products.
2.2
animal feeding stuff
any substance or product, including additives, whether processed, partially processed or unprocessed,
intended to be used for oral feeding to animals
EXAMPLES Raw materials, fodder, animal flour, mixed feed and other end products, and pet food.
2.3
constituent content
mass fraction of substances determined using the appropriate, standardized or validated chemical method
NOTE 1 The mass fraction is often expressed as a percentage.
NOTE 2 Examples of constituents determined include moisture, fat, protein, crude fibre, neutral detergent fibre, and
acid detergent fibre. For appropriate methods, see, e.g., References [1] to [16].
2.4
technological parameter
property or functionality of a matrix that can be determined using the appropriate standardized or validated
method(s)
EXAMPLE Digestibility.
NOTE 1 In the context of this International Standard, the matrices are animal feeding stuffs, cereals and milled cereal
products.
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SIST EN ISO 12099:2010
ISO 12099:2010(E)
NOTE 2 It is possible to develop and validate NIR methods for other parameters and matrices than listed, as long as
the procedure from this International Standard is observed. The measuring units of the parameters determined have to
follow the units used in the reference methods.
3 Principle
Spectral data in the near infrared (NIR) region are collected and transformed to constituent or parameter
concentrations by calibration models developed on representative samples of the products concerned.
4 Apparatus
4.1 Near-infrared instruments, based on diffuse reflectance or transmittance measurement covering the
−1 −1
NIR wavelength region, 770 nm to 2 500 nm (12 900 cm to 4 000 cm ), or segments of this or at selected
wavelengths or wavenumbers. The optical principle may be dispersive (e.g. grating monochromators),
interferometric or non-thermal (e.g. light-emitting diodes, laser diodes, and lasers). The instrument should be
provided with a diagnostic test system for testing photometric noise and reproducibility, wavelength or
wavenumber accuracy and wavelength or wavenumber precision (for scanning spectrophotometers).
The instrument should measure a sufficiently large sample volume or surface to eliminate any significant
influence of inhomogeneity derived from chemical composition or physical properties of the test sample. The
sample pathlength (sample thickness) in transmittance measurements should be optimized according to the
manufacturer's recommendation with respect to signal intensity for obtaining linearity and maximum
signal/noise ratio. In reflectance measurements, a quartz window or other appropriate material to eliminate
drying effects should preferably cover the interacting sample surface layer.
4.2 Appropriate milling or grinding device, for preparing the sample (if needed).
NOTE Changes in grinding or milling conditions can influence NIR measurements.
5 Calibration and initial validation
5.1 General
The instrument has to be calibrated before use. Because a number of different calibration systems can be
applied with NIR instruments, no specific procedure can be given for calibration.
For an explanation of methods for calibration development see, for example, Reference [17] and appropriate
manufacturers' manuals. For the validation, it is important to have a sufficient number of representative
samples, covering variations such as:
a) combinations and composition ranges of major and minor sample components;
b) seasonal, geographic and genetic effects on forages, feed raw materials and cereals;
c) processing techniques and conditions;
d) storage conditions;
e) sample and instrument temperature;
f) instrument variations (differences between instruments).
NOTE For a solid validation at least 20 samples are needed.
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ISO 12099:2010(E)
5.2 Reference methods
Internationally accepted reference methods for determination of moisture, fat, protein, and other constituents
and parameters should be used. See References [1] to [16] for examples.
The reference method used for calibration should be in statistical control, i.e. for any sample, the variability
should consist of random variations of a reproducible system. It is essential to know the precision of the
reference method.
5.3 Outliers
In many situations, statistical outliers are observed during calibration and validation. Outliers may be related to
NIR data (spectral outliers, hereafter referred to as x-outliers) or errors in reference data or samples with a
different relationship between reference data and NIR data (hereafter referred to as y-outliers)
(see Figures B.1 to B.5).
For the purpose of validation, samples are not to be regarded as outliers if:
a) they are within the working range of the constituents/parameters in the calibration(s);
b) they are within the spectral variation of the calibration samples, e.g. as estimated by Mahalanobis
distance;
c) the spectral residual is below a limit defined by the calibration process;
d) the prediction residual is below a limit defined by the calibration process.
If a sample appears as an outlier then it should be checked initially to see if it is an x-outlier. If it exceeds the
x-outlier limits defined for the calibration it should be removed. If it is not an x-outlier, then both the reference
value and the NIR predicted value should be checked. If these confirm the original values then the sample
should not be deleted and the validation statistics should include this sample. If the repeat values show that
either the original reference values or the NIR predicted ones were in error then the new values should be
used.
5.4 Validation of calibration models
5.4.1 General
Before use, calibration equations shall be validated locally 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 6.5) at least 20 samples are needed. Validation shall
be carried out for each sample type, constituent or parameter, and temperature. The calibration is valid only
for the variations, i.e. sample types, range and temperature, used in the validation.
Results obtained on the independent test set are plotted, reference against NIR, and residuals against
reference results, to give a visual impression of the performance of the calibration. The SEP is calculated
(see 6.5) and the residual plot of data corrected for mean systematic error (bias) is examined for outliers, i.e.
samples with a residual exceeding ± 3s .
SEP
If the validation process shows that the model cannot produce acceptable statistics, then it should not be used.
NOTE What is acceptable depends on such criteria as the performance of the reference method, the range covered,
and the purpose of the analysis and is up to the parties involved to decide.
The next step is to fit NIR data, y , and reference data, y , by linear regression (y = by + a) to produce
NIR ref ref NIR
statistics that describe the validation results.
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SIST EN ISO 12099:2010
ISO 12099:2010(E)
5.4.2 Bias correction
The data are also examined for bias between the methods. If the difference between means of the NIR
predicted and reference values is significantly different from zero then this indicates that the calibration is
biased. A bias may be removed by adjusting the constant term (see 6.3) in the calibration equation.
5.4.3 Slope adjustment
If the slope, b, is significantly different from 1, the calibration is skewed.
Adjusting the slope or intercept of the calibration is generally not recommended unless the calibration is
applied to new types of samples or instruments. If a reinvestigation of the calibration does not detect outliers,
especially outliers with high leverage, it is preferable to expand the calibration set to include more samples.
However, if the slope is adjusted, the calibration should then be tested on a new independent test set.
5.4.4 Expansion of calibration set
If the accuracy of the calibration does not meet expectations, the calibration set should be expanded to
include more samples or a new calibration performed. In all cases, when a new calibration is developed on an
expanded calibration set, the validation process should be repeated on a new validation set. If necessary,
expansion of the calibration set should be repeated until acceptable results are obtained on a validation set.
5.5 Changes in measuring and instrument conditions
Unless additional calibration is performed, a local validation of an NIR method stating the accuracy of the
method can generally not be considered valid if the test conditions are changed.
For example, calibrations developed for a certain population of samples may not be valid for samples outside
this population, although the analyte concentration range is unchanged. A calibration developed on grass
silages from one area may not give the same accuracy on silages from another area if the genetic, growing
and processing parameters are different.
Changes in the sample presentation technique or the measuring conditions (e.g. temperature) not included in
the calibration set may also influence the analytical results.
Calibrations developed on a certain instrument cannot always be transferred directly to an identical instrument
operating under the same principle. It may be necessary to perform bias, slope or intercept adjustments to
calibration equations. In many cases, it is necessary to standardize the two instruments against each other
before calibration equations can be transferred (Reference [17]). Standardization procedures can be used to
transfer calibrations between instruments of different types provided that samples are measured in the same
way (reflectance, transmittance) and that the spectral region is common.
If the conditions are changed, a supplementary validation should be performed.
The calibrations should be checked whenever any major part of the instrument (optical system, detector) has
been changed or repaired.
6 Statistics for performance measurement
6.1 General
The performances of a prediction model shall be determined by a set of validation samples. This set consists
of samples which are independent of the calibration set. In a plant, it is new batches; in agriculture, it is a new
crop or a new experiment location.
This set of samples shall be carefully analysed following the reference methods. Care is essential in analysing
validation samples and the precision of these results is more important for the validation set than for the
samples used at the calibration phase.
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SIST EN ISO 12099:2010
ISO 12099:2010(E)
The number of validation samples shall be at least 20 to compute the statistics with some confidence.
6.2 Plot the results
It is important to visualize the results in plots, i.e. reference vs predicted values or residuals vs predicted
values.
The residuals are defined as:
ey=−y (1)
ii i
where
y is the ith reference value;
i
y is the ith predicted value obtained when applying the multivariate NIR model.
i
The way the differences are calculated gives a positive bias when the predictions are too high and a negative
one when the predictions are too low compared to the reference values.
A plot of the data gives an immediate overview of the correlation, the bias, the slope, and the presence of
obvious outliers (see Figure 1).
Key
1 45° line (ideal line with bias, e = 0 and slope, b = 1) a intercept
2 45° line displaced by bias, e e bias
3 linear regression line with y -intercept, a y near infrared spectroscopy predicted values
ref NIRS
4 outliers y reference value
ref
NOTE The outliers have a strong influence on the calculation of the slope and should be removed if the results are to
be used for adjustments.
Figure 1 — Scatter plot for a validation set, y = f(a + by )
ref NIRS
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SIST EN ISO 12099:2010
ISO 12099:2010(E)
6.3 The bias
Most of the time, a bias or systematic error is what is observed with NIR models. Bias can occur due to: new
samples of a type not previously seen by the model, drift of the instrument, drift in wet chemistry, changes in
the process, and changes in the sample preparation.
With the number of independent samples, n, the bias (or offset) is the mean difference, e , and can be defined
as:
n
1
ee= (2)
i
∑
n
i=1
where e is the residual as defined in Equation (1), or
i
nn
⎛⎞
1
⎜⎟
ey=−y=y−y (3)
∑∑ii
⎜⎟
n
ii==11
⎝⎠
where
y is the ith reference value;
i
y is the ith predicted value obtained when applying the multivariate NIR model;
i
and
y is the mean of the predicted values;
y is the mean of the reference values.
The significance of the bias is checked by a t-test. The calculation of the bias confidence limits (BCLs), T ,
b
determines the limits for accepting or rejecting equation performance on the small set of samples chosen from
the new population.
ts
SEP
1/−α2
()
T =± (4)
b
n
where
α is the probability of making a type I error;
t is the appropriate Student t-value for a two-tailed test with degrees of freedom associated with
SEP and the selected probability of a type I error (see Table 1);
n is the number of independent samples;
s is the standard error of prediction (see 6.5).
SEP
EXAMPLE With n = 20, and s = 1, the BCLs are
SEP
2, 09 × 1
T =± =± 0, 48 (5)
b
20
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SIST EN ISO 12099:2010
ISO 12099:2010(E)
This means that the bias tested with 20 samples must be higher than 48 % of the standard error of prediction
to be considered as different from zero.
Table 1 — Values of the t-distribution with a probability, α = 0,05 = 5 %
n t n t n t n t
5 2,57 11 2,20 17 2,11 50 2,01
6 2,45 12 2,18 18 2,10 75 1,99
7 2,36 13 2,16 19 2,09 100 1,98
8 2,31 14 2,14 20 2,09 200 1,97
9 2,26 15 2,13 30 2,04 500 1,96
10 2,23 16 2,12 40 2,02 1 000 1,96
1)
NOTE The Excel function TINV can be used.
6.4 Root mean square error of prediction (RMSEP)
The RMSEP, s , (C.3.6) is expressed mathematically as
RMSEP
n
2
e
∑ i
i=1
s = (6)
RMSEP
n
where
e is the residual of the ith sample;
i
n is the number of independent samples.
This value can be compared with SEC (C.3.3) and SECV (C.3.4).
RMSEP includes the random error (SEP) and the systematic error (bias). It also includes the error of the
reference methods (as do SEC and SECV).
(1n −)
22
s =+se (7)
RMSEP SEP
n
where
n is the number of independent samples;
s is the standard error of prediction (see 6.5);
SEP
e is the bias or systematic error.
There is no direct test for RMSEP. This is the reason for separating the systematic error, bias or e , and the
random error, SEP or s .
SEP
1) Excel is the trade name of a product supplied by Microsoft. This information is given for the convenience of users of
this document and does not constitute an endorsement by ISO of the product named. Equivalent products may be used if
they can be shown to lead to comparable results.
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SIST EN ISO 12099:2010
ISO 12099:2010(E)
6.5 Standard error of prediction (SEP)
The SEP, s , or the standard deviation of the residuals, which expresses the accuracy of routine NIR results
SEP
corrected for the mean difference (bias) between routine NIR and reference method, can be calculated by
using Equation (8):
n
2
ee−
()
∑ i
i=1
s = (8)
SEP
n −1
where
n is the number of independent samples;
e is the residual of the ith sample;
i
e is the bias or systematic error.
The SEP should be related to the SEC (C.3.3) or SECV (C.3.4) to check the validity of the calibration model
for the selected validation set.
The unexplained error confidence limits (UECLs), T , are calculated from an F-test (ratio of 2 variances)
UE
(see Reference [19] and Table 2).
Ts= F (9)
UE SEC (α:vM, )
where
s is the standard error of calibration (C.3.3);
SEC
α is the probability of making a type I error;
ν = n − 1 is the numerator degrees of freedom associated with SEP of the test set, in which n is
the number of samples in the validation process;
M = n − p − 1 is the denominator degrees of freedom associated with SEC (standard
...
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