Task for a laboratory
Determination of moisture content (water content) in edible oil
using the volumetric Karl Fischer method
according to ISO 8534:1996
PART I.
Description of the analytical procedure
PART II
The customer's requirement concerning quality of the measurement result
PART III.
Validation data of the measurement procedure – relevant equations and measurement data
PART IV.
Measurement uncertainty of the result – relevant equations and measurement data
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PART I. Description of the analytical procedure
0. Principle
Karl Fischer (KF) titration is based on the following reaction:
I2 +
N SO2 + 2
N + H2O
N SO3 + ROH
→
→
2
N H + I– +
N H + ROSO3–
N SO3
(1)
(2)
The reaction is very fast and with strict stoichiometry. Solution of iodine, SO2 and pyridine
dissolved the alcohol ROH is the titrant solution. In the classical KF titrant the alcohol ROH is
methanol. In modern commercial titrants ROH is often methoxyethanol and pyridine is often
replaced by imidazole (both because of potential toxicity). Titration is carried out in ROH or in a
mixture of ROH and some other solvent (if samples are not soluble in ROH).
The end point of the titration is indicated by a small amount of unreacted iodine in solution. Endpoint is usually determined voltammetrically: alternating current of constant strength is applied to a
double Pt electrode. Potential difference between the Pt wires is monitored. Even small quantities
of iodine lead to a dramatic drop of the potential difference.
1. Scope
The procedure is suitable for moisture content (water content) determination in edible oils in the
range of 50 to 2000 mg/kg.
2. Procedure
KF titration is carried out in a tightly closed (to minimize sample contamination by atmospheric
moisture) magnetically stirred titration cell in a solvent (mixture of methoxyethanol and chloroform).
The cell is not emptied after each sample. Samples are titrated one after another in the same cell
until the cell is full.
1. Before the first titration the solvent is titrated to the end-point (to remove traces of water
that have diffused into the cell).
2. 5-20 g of oil is weighed in a syringe to nearest 0.01 g.
3. The sample is injected into the titration cell
4. The empty syringe is weighed again to determine the mass of the oil sample.
5. When the sample has dissolved then the sample is titrated to the end-point.
6. Concentration of water in the sample is calculated from the titration data.
The concentration of KF titrant solution is usually expressed as titer with respect to water. The titer
is determined by titrating either a standard solution of water or a solution of some salt containing an
exact amount of hydrate water in ROH. The titer of freshly prepared KF titrant is around 5 mg of
water per 1 ml of titrant. Every care is taken to protect the titrant solution from atmospheric
moisture. Nevertheless, the titer of the titrant solution decreases with time and it is necessary
redetermine it daily or every second day.
3. Interferences
Many compounds (strong oxidizing and reducing agents, aldehydes, etc) can interfere with KF
reaction. However, no such compounds are present in edible oils. Thus within the scope of
application there are no interferences.
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4. Equipment and Reagents
Titrator
Volumetric KF-titrator capable of air-tight storage
of the titrant and dispensing titrant in 0.002 ml
steps, with voltammetric end point detection.
Solvent
Mixture of methoxyethanol and chloroform.
Titrant
Commercial
composition.
KF
titrant
with
proprietary
5. Sampling and sample pre-treatment
The oil sample is shaken to ensure its
homogeneity and a suitable amount is aspired
into an air-tight plastic syringe.
6. Calculation
Calculation of water content in sample is carried out according to the following equation
(mathematical model):
cwater 
Vt  Tt
 1000
ms
(3)
cwater is concentration of water in oil sample [mg/kg], Vt is volume of titrant used for titration [ml], Tt
is the titer of the titrant (mass of water per unit volume of titrant) [mg/ml], ms is the sample mass [g],
1000 [g/kg] is the unit conversion factor.
7. Results
The result is presented with number of decimal digits corresponding to the obtained uncertainty.
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PART II. The customer's requirement concerning quality of the measurement
result
In this case the customer is a producer of refined rapeseed oil. The measurement is needed to test
if the moisture content in the oil under question is below or above the maximum permissible limit,
which is 500 mg/kg. This permissible limit has been set by the quality standard of the producer.
PART III. Validation data of the measurement procedure – relevant equations
and measurement data
1. Equations.
Pooled standard deviation:
(n1  1) s1  (n2  1) s2  ...  (nk  1) sk
spooled 
n1  n2  ...  nk  k
2
k
s1, s2, …
n1, n2, …
2
2
(4)
number of data groups
within group standard deviations
numbers of measurements in groups
2. Validation Data
Table 1. Repeatability data.
Sample 1
Sample 2
Sample 3
Sample 4
Sample 5
Values
(mg/kg)
77.6
76.6
87.1
115.6
125.3
125.7
181.6
183.6
187.4
249.3
255.4
253.1
392.3
389.1
378.1
Average
Std Dev
80.4
5.8
122.2
5.8
184.2
2.9
252.6
3.1
386.5
7.4
The data are obtained on different days with different samples. From these data the pooled
repeatability standard deviation sr can be calculated. It can be seen that there is no correlation
between the water content and standard deviation. Thus sr can be assumed to be reasonably
constant over the whole concentration range.
Table 2. Participation in Interlaboratory Comparisons.
No
ref value
s
No of
labs
lab
1
2
3
4
363
88
82
374
33
19
22
21
7
5
7
9
381
96
80
365
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PART IV. Measurement uncertainty of the result – relevant equations and
measurement data
1. Equations for uncertainty calculation by the ISO GUM Modelling (bottom up) approach
The equation to use for the ISO GUM modelling approach is similar to the equation used for
calculating the result. There is only one difference: the repeatability components of Vt and ms are
not included in the uncertainties of these quantities but are taken into account separately as a
repeatability factor fr. The reason is the following: the amount of injected sample is measured
gravimetrically and each time the sample amount is slightly different.
The equation on which the uncertainty calculation is based is the following:
Vt  Tt
 f r  1000
ms
cwater 
(5)
The value of fr is 1 (without units). Its standard uncertainty u(fr) is found as follows:
u( f r ) 
sr
(6)
cwater
sr can be found from the repeatability data (Table 1).
The combined standard uncertainty of cwater is found as follows:
2
uc (cwater )  cwater
2
2
 u (Vt )   u (Tt )   u (ms )   u ( f r ) 

  
  
  

V
T
m
f
 t   t   s   r 
2
(7)
2. Measurement data for uncertainty calculation using the ISO GUM Modelling approach
Table 3. Measurement data.
Input quantity or
component
Vt
u(Vt, cal)
u(Vt, temp)
u(Vt, rep)
Tt
ms
u(ms, drift)
u(ms, nonlin)
u(ms, rep)
fr
Unit
Value
ml
ml
ml
ml
mg/ml
g
g
g
g
Unitless
0.720
4.9987
9.7734
1
Uncertainty
Type of
uncertainty
0.004
0.002
–
0.05
Rectangular
Standard
0.0002
0.0002
–
Rectangular
Rectangular
Comments
Taken into account by fr
Standard
Taken into account by fr
Uncertainty to be found
from repeatability data
Table 3 presents the data relevant for the particular measurement for which the uncertainty is
calculated.
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3. Equations for uncertainty calculation using in-house validation data
The notations are the same is used in the Nordtest handbook for uncertainty evaluation 1.
uc  u( Rw ) 2  u(bias ) 2
(8)
u(Rw) is the uncertainty component that takes into account precision, within lab reproducibility.
u(bias) is the uncertainty component that takes into account the trueness, i.e. the (possible) lab
bias. The trueness estimate in turn is composed of two components:
u (bias )  RMS bias  u (Cref ) 2
2
(9)
RMSbias is the root mean of bias.
u(Cref) is the uncertainty of the reference value against which the bias is found.
RMS bias 
 (bias )
2
i
(10)
n
biasi is the bias found on i-th bias determination (difference between lab result and reference
value).
If interlaboratory comparisons are used then u(Cref) is found as follows:
u (Cref ) 
 (u(Cref ) )
2
i
n
(11)
n is the number of interlaboratory comparisons where the laboratory has participated.
U(Cref)i is the uncertainty of the reference value (or consensus value) of the i-th interlaboratory
comparison.
u (Cref ) i 
si
ni
(12)
si is the standard deviation of participant results of the i-th intercomparison
ni is the number of participant results from which the si value was obtained.
4. Data for uncertainty calculation using in-house validation data
In this case the pooled standard deviation sr (data from Table 1) can be used as an estimate of
u(Rw).
Interlaboratory comparison data (Table 2) are used for finding RMSbias and u(Cref).
1
Handbook for calculation of measurement uncertainty in environmental laboratories (2004) Nordtest TR
537, Espoo, Finland (available from the Internet: http://www.nordicinnovation.net/nordtestfiler/tec537.pdf)
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