EN ISO 12099:2010

SLOVENSKI STANDARD
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
2003-01.Slovenski inštitut za standardizacijo. Razmnoževanje celote ali delov tega standarda ni dovoljeno.

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,
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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.

Contents Page
Foreword .3

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.
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
ISO 12099:2010(E)
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ii © ISO 2010 – All rights reserved

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

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

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).
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.
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.
2 © ISO 2010 – All rights reserved

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.
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.
4 © ISO 2010 – All rights reserved

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
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
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
6 © ISO 2010 – All rights reserved

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
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 −)
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.
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
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 error of
c
calibration);
in which
n is the number of calibration samples,
c
p is the number of terms or PLS factors of the model.
NOTE 1 SEC can be replaced by SECV which is a better statistic than SEC; very often SEC is too optimistic,
s > s .
SECV SEC
EXAMPLE With n = 20, α = 0,05, M = 100, and s = 1,
SEC
T = 1, 30 (10)
UE
This means that, with 20 samples, a SEP can be accepted that is up to 30 % larger than the SEC.
2)
NOTE 2 The Excel function FINV can be used.

2) 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 the same results.
8 © ISO 2010 – All rights reserved

ISO 12099:2010(E)
The F-test cannot be used to compare two calibrations on the same validation set. It needs (as here) two
independent sets to work. Another test is required to compare two or more models on the same data set.
Table 2 — F-values and square root of the F-values as a function of the degrees of freedom of the
numerator associated with SEP and of the denominator associated with SEC
[see definitions under Equation (9)]
F √[F ]
(α: ν, M) (α: ν, Μ)
Degrees Degrees of freedom (SEC) Degrees Degrees of freedom (SEC)
of of
50 100 200 500 1 000 50 100 200 500 100
freedom freedom
(SEP) (SEP)
5 2,40 2,31 2,26 2,23 2,22 5 1,55 1,52 1,50 1,49 1,49
6 2,29 2,19 2,14 2,12 2,11 6 1,51 1,48 1,46 1,45 1,45
7 2,20 2,10 2,06 2,03 2,02 7 1,48 1,45 1,43 1,42 1,42
8 2,13 2,03 1,98 1,96 1,95 8 1,46 1,43 1,41 1,40 1,40
9 2,07 1,97 1,93 1,90 1,89 9 1,44 1,41 1,39 1,38 1,37
10 2,03 1,93 1,88 1,85 1,84 10 1,42 1,39 1,37 1,36 1,36
11 1,99 1,89 1,84 1,81 1,80 11 1,41 1,37 1,36 1,34 1,34
12 1,95 1,85 1,80 1,77 1,76 12 1,40 1,36 1,34 1,33 1,33
13 1,92 1,82 1,77 1,74 1,73 13 1,39 1,35 1,33 1,32 1,32
14 1,89 1,79 1,74 1,71 1,70 14 1,38 1,34 1,32 1,31 1,30
15 1,87 1,77 1,72 1,69 1,68 15 1,37 1,33 1,31 1,30 1,29
16 1,85 1,75 1,69 1,66 1,65 16 1,36 1,32 1,30 1,29 1,29
17 1,83 1,73 1,67 1,64 1,63 17 1,35 1,31 1,29 1,28 1,28
18 1,81 1,71 1,66 1,62 1,61 18 1,30 1,31 1,29 1,27 1,27
19 1,80 1,69 1,64 1,61 1,60 19 1,34 1,30 1,28 1,27 1,26
29 1,69 1,58 1,52 1,49 1,48 29 1,30 1,26 1,23 1,22 1,22
49 1,60 1,48 1,42 1,38 1,37 49 1,27 1,22 1,19 1,17 1,17
99 1,53 1,39 1,32 1,28 1,26 99 1,24 1,18 1,15 1,13 1,12
199 1,48 1,34 1,26 1,21 1,19 199 1,22 1,16 1,12 1,10 1,09
499 1,46 1,31 1,22 1,16 1,13 499 1,21 1,14 1,11 1,08 1,07
999 1,45 1,30 1,21 1,14 1,11 999 1,20 1,14 1,10 1,07 1,05

6.6 Slope

The slope, b, of the simple regression y = a + by is often reported in NIR publications.
Notice that the slope must be calculated with the reference values as the dependent variable and the
predicted NIR values as the independent variable, if the calculated slope is intended to be used for adjustment
of NIR results (like in the case of the inverse multivariate regression used to build the prediction model).
ISO 12099:2010(E)
From the least squares fitting, the slope is calculated as:

s
yy
b = (11)
s
yˆ
where

s is the covariance between reference and predicted values;
yy
s is the variance of the n predicted values.
ˆ
y
The intercept is calculated as:
ay=−byˆ (12)
where

y is the mean of the predicted values;
y is the mean of the reference values;
b is the slope.
As for the bias, a t-test can be calculated to check the hypothesis that b =1
sn(1−)
yˆ
tb=− 1 (13)
obs
s
res
where
n is the number of independent samples;
s is the variance of the n predicted values;
yˆ
s is the residual standard deviation as defined in Equation (14).
res
n
ya−+byˆ
()
∑ii
i=1
s = (14)
res
n − 2
in which
n is the number of independent samples,
a is the intercept Equation (12),
b is the slope Equation (11),
y is the ith reference value,
i

y is the ith predicted value obtained when applying the multivariate NIR model.
i
10 © ISO 2010 – All rights reserved

ISO 12099:2010(E)
(RSD is like the SEP when the predicted values are corrected for slope and intercept. Do not confuse bias and
intercept — see also Figure 1.) The bias equals the intercept only when the slope is exactly one.
The slope, b, is considered as different from 1 when
ttW
obs (1−α / 2)
where
t is the observed t-value, calculated according to Equation (13);
obs
t is the t-value obtained from Table 1 for a probability of α = 0,05 (5 %).
(1-α/2)
Too narrow a range or an uneven distribution leads to inappropriate correction of the slope even when the
SEP is correct. The slope can only be adjusted when the validation set covers a large part of the calibration
range.
EXAMPLE For n = 20 samples with a residual standard deviation [Equation (14)] of 1, a standard deviation of the
predicted values of s = 2 and a calculated slope of b = 1,2, the observed t value is 1,7 and then the slope is not
ˆ
y obs
significantly different from 1 as the t-value (see Table 1) for n = 20 samples is 2,09. If the slope is 1,3, the t value is 2,6
obs
and then the slope is significantly different from 1.
7 Sampling
Sampling is not part of the method specified in this International Standard. Recommended sampling
[5] [16]
procedures are given in ISO 6497 and ISO 24333 .
It is important that the laboratory receive a truly representative sample which has not been damaged or
changed during transport or storage.
8 Procedure
8.1 Preparation of test sample
All laboratory samples should usually be kept under conditions that maintain the composition of the sample
from the time of sampling to the time of commencing the procedure.
Samples for routine measurements should be prepared in the same way as validation samples. It is necessary
to apply standard conditions.
Before the analysis, the sample should be taken in such a way as to obtain a sample representative of the
material to be analysed.
For specific procedures, see specific NIR standards.
8.2 Measurement
Follow the instructions of the instrument manufacturer or supplier.
The prepared sample should reach a temperature within the range included in the validation.
ISO 12099:2010(E)
8.3 Evaluation of result
To be valid, routine results shall be within the range of the calibration model used.
Results obtained on samples detected as spectral outliers cannot be regarded as reliable.
9 Checking instrument stability
9.1 Control sample
At least one control sample should be measured at least once per day to check instrument hardware stability
and to detect any malfunction. Knowledge of the true concentration of the analyte in the control sample is not
necessary. The sample material should be stable and, as far as possible, resemble the samples to be
analysed. The parameter(s) measured should be stable and, as far as possible, identical to or at least
biochemically close to the sample analyte. A sample is prepared as in 8.1 and stored in such a way as to
maximize the storage life. These samples are normally stable for lengthy periods, but the stability should be
tested in the actual cases. Control samples should be overlapped to secure uninterrupted control.
The recorded day-to-day variation should be plotted in control charts and investigated for significant patterns
or trends.
9.2 Instrument diagnostics
For scanning spectrophotometers, the wavelength or wavenumber (see 4.1) accuracy and precision should be
checked at least once a week or more frequently if recommended by the instrument manufacturer, and the
results should be compared to specifications and requirements (4.1).
A similar check of the instrument noise shall be carried out weekly or at intervals recommended by the
manufacturer.
9.3 Instruments in a network
If several instruments are used in a network, special attention has to be given to standardization of the
instruments according to the manufacturer's recommendations.
10 Running performance check of calibration
10.1 General
The suitability of the calibration for the measurement of individual samples should be checked. The outlier
measures used in the calibration development and validation can be applied, e.g. Mahalanobis distance and
spectral residuals. In most instruments, this is done automatically.
If the sample does not pass the test, i.e. the sample does not fit into the population of the samples used for
calibration and/or validation, it cannot be determined by the prediction model, unless the model is changed.
Thus the outlier measures can be used to decide which samples should be selected for reference analysis
and included in a calibration model update.
If the calibration model is found to be suitable for the measured sample, the spectrum is evaluated according
to the validated calibration model.
NIR methods should be validated continuously against reference methods to secure steady optimal
performance of calibrations and observance of accuracy. The frequency of checking the NIR method should
be sufficient to ensure that the method is operating under steady control with respect to systematic and
random deviations from the reference method. The frequency depends inter alia on the number of samples
analysed per day and the rate of changes in sample population.
12 © ISO 2010 – All rights reserved

ISO 12099:2010(E)
The running validation should be performed on samples selected randomly 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, e.g. segmentation of concentration range and random selection of test samples within
each segment or to ensure that samples with a commercially important range are covered.
The number of samples for the running validation should be sufficient for the statistics used to check the
performance. For a solid validation, at least 20 samples are needed (to expect a normal distribution of
variance). One can fill in the results of the independent validation set for starting the running validation. To
continue about 5 to 10 samples every week is quite sufficient to monitor the performance properly. Using
fewer samples, it is hard to take the right decision in case one of the results is outside the control limits.
10.2 Control charts using the difference between reference and NIR results
Results should be assessed by control charts, plotting running sample numbers on the abscissa and the
difference between results obtained by reference and NIR methods on the ordinate; ± 2s (95 % probability)
SEP
and ± 3s (99,8 % probability) may be used as warning and action limits where the SEP has been obtained
SEP
on a test set collected independently of calibration samples.
If the calibration and the reference laboratories are performing as they should, then only one point in 20 points
should plot outside the warning limits and two points in 1 000 points outside the action limits.
Control charts should be checked for systematic bias drifts from zero, systematic patterns, and excessive
variation of results. General rules applied for Shewart control charts may be used in the assessment (see
[7]
ISO 8258 ). However, too many rules applied simultaneously may result in too many false alarms.
The following rules used in combination have proved to be useful in detection of problems:
a) one point outside either action limit;
b) two out of three points in a row outside a warning limit;
c) nine points in a row on the same side of the zero line.
Additional control charts plotting other features of the running control (e.g. mean difference between NIR and
[8]
reference results, see ISO 9622 ) and additional rules may be applied to strengthen decisions.
In the assessment of results, it should be remembered that SEP and measured differences between NIR and
reference results also include the imprecision of reference results. This contribution can be neglected if the
imprecision of reference results is reduced to less than one-third of the SEP (see Reference [19]).
To reduce the risk of false alarms, the control samples should be analysed independently (in different series)
by both NIR spectrometry and reference methods to avoid the influence of day-to-day systematic differences
in reference analyses, for example.
If the warning limits are often exceeded and the control chart only shows random fluctuations (as opposed to
trends or systematic bias), the control limits may have been based on a SEP value that is too optimistic. An
attempt to force the results within the limits by frequent adjustments of the calibration does not improve the
situation in practice. The SEP should instead be re-evaluated using the latest results.
If the calibration equations after a period of stability begin to move out of control, the calibration should be
updated. Before this is done, an evaluation should be made of whether the changes could be due to changes
in reference analyses, unintended changes in measuring conditions (e.g. caused by a new operator),
instrument drift or malfunction etc. In some cases, a simple adjustment of the constant term in the calibration
equation may be sufficient (an example is shown in Figure B.6). In other cases it may be necessary to run a
complete re-calibration procedure, where the complete or a part of the basic calibration set is expanded to
include samples from the running validation, and perhaps additional samples selected for this purpose (an
example is shown in Figure B.7).
ISO 12099:2010(E)
Considering that the reference analyses are in statistical control and the measuring conditions and instrument
performance are unchanged, significant biases or increased SEP values can be due to changes in the
chemical, biological or physical properties of the samples compared to the underlying calibration set.
Other control charts, e.g. using z-scores, may be used.
11 Precision and accuracy
11.1 Repeatability
The repeatability, i.e. the difference between two individual single test results, obtained with 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, which should not be exceeded in more than 5 % of cases, depends on th
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