ISO 14488:2007/Amd 1:2019
(Amendment)Particulate materials — Sampling and sample splitting for the determination of particulate properties — Amendment 1
Particulate materials — Sampling and sample splitting for the determination of particulate properties — Amendment 1
Matériaux particulaires — Échantillonnage et division des échantillons pour la caractérisation des propriétés particulaires — Amendement 1
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INTERNATIONAL ISO
STANDARD 14488
First edition
2007-12-15
AMENDMENT 1
2019-11
Particulate materials — Sampling and
sample splitting for the determination
of particulate properties
AMENDMENT 1
Matériaux particulaires — Échantillonnage et division des
échantillons pour la caractérisation des propriétés particulaires
AMENDEMENT 1
Reference number
ISO 14488:2007/Amd.1:2019(E)
©
ISO 2019
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ISO 14488:2007/Amd.1:2019(E)
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ISO 14488:2007/Amd.1:2019(E)
Foreword
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sieving, Subcommittee SC 4, Particle characterization.
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© ISO 2019 – All rights reserved iii
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ISO 14488:2007/Amd.1:2019(E)
Particulate materials — Sampling and sample splitting for
the determination of particulate properties
AMENDMENT 1
Annex B
Add the following additional clause.
B.7 Simple approach to the calculation of the fundamental sampling error (FSE) and minimum
mass required for a specified standard error
This clause provides a method to approximately determine the magnitude (mg/g/kg) of the minimum
mass required to meet a specific standard error. The approach approximates the precise calculations
described in the document. With a knowledge or assumption of the actual mass utilized in the particle
size analysis experiment, the best standard error achievable based solely on the heterogeneity of the
material can also be estimated.
The fundamental sampling error (FSE) is one of 8 errors (implies culpability) or variables (implies
[12]
statistical variation) originally described by Pierre Gy – see Table B.3. It represents the smallest
possible variation in sample to sample reproducibility based solely on the heterogeneity of the material
or distribution involved.
Table B.3 — Old (Gy) and new notations (Pitard/Esbensen)
Errors Old term New term
Heterogeneity fluctuation error CE HFE
Quality fluctuation error QE QFE
Fundamental sampling error FE FSE
Grouping and segregation error GE GSE
Increment weighting error WE IWE
Increment delimitation error DE IDE
Increment extraction error EE IEE
Increment preparation errors PE IPE
[13][14][15]
The FSE is identical to the standard error familiar to mathematicians and statisticians . The
standard error (SE) of a parameter is the standard deviation (σ, theoretical value) or an estimate of
the standard deviation (s, absolute value coming from measurements) of a sampled distribution. If the
parameter or the statistic is the mean, it is called the standard error of the mean (SEM). It represents
how close the result gets to the ‘true’ mean with repeated samplings or an increased proportion of the
actual distribution.
In particle size distribution considerations, the FSE is inversely proportional to the square root of the
number of particles present in the distribution or part of the distribution. In the following derivations,
no assumption is made of the form of the original particle size distribution; only, that the samples
withdrawn are normally distributed (as is the standard case).
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ISO 14488:2007/Amd.1:2019(E)
2
SEM α 1/√n or n α 1/σ
For 1 % SEM it can be shown that:
2
n = 1/(0,01) = 10 000
Thus, 10 000 particles in total will be needed to specify the mean to 1 % SE. See also Reference [18]. The
implication is that to specify any other point of the distribution to 1 % SE, at least 10 000 particles will
be needed in the portion of the distribution above that point. The worst-case situation is considered
first; specifying the x point in the distribution to a standard error of 1 %. This requires 10 000
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particles in the x + part of the distribution. This x + part represents only 1/100 of the total sample
99 99
mass of the entire distribution. Thus, only the mass (= volume × density) of 10 000 particles needs to be
calculated at some known or assumed x point in the distribution and multiplied by the appropriate
99
value to compute the total mass of the distribution. Using the value of the x point is more convenient
99
than attempting to use a more correct midpoint between the x and (unknown) x point. It will
99 100
slightly underestimate the minimum mass required but calculations show this to be minor.
Making two assumptions, the minimum mass of the portion M can be calculated: the particles are
Q
spherical and all particles of the portion have the minimum diameter x .
Q
3
Mn=×(/π 6)××x ρ
Qmin Q
where
n is the minimum number of particles for the specified precision;
min
(π/6) is the shape factor for a sphere;
x is the largest point in
...
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