Trainmic Example Summary Fiche

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

1 PART I. Description of the analytical procedure

0. Principle Karl Fischer (KF) titration is based on the following reaction:

+ N N N H – N I2 + SO2 + 2 + H2O → 2 I + SO3 (0)

+ N N H – SO3 + ROH → ROSO3 (0)

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. End- point 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

2 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.

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 KF titrant with proprietary composition.

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):

Vt Tt cwater  1000 (0) ms

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.

3 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:

2 2 2 (n1 1)s1  (n2 1)s2 ... (nk 1)sk spooled  (0) n1  n2 ... nk  k k number of data groups s1, s2, … within group standard deviations n1, n2, … numbers of measurements in groups

2. Validation Data

Table 1. Repeatability data.

Sample 1 Sample 2 Sample 3 Sample 4 Sample 5 Values 77.6 115.6 181.6 249.3 392.3 (mg/kg) 76.6 125.3 183.6 255.4 389.1 87.1 125.7 187.4 253.1 378.1

Average 80.4 122.2 184.2 252.6 386.5 Std Dev 5.8 5.8 2.9 3.1 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 of No ref value s labs lab 1 363 33 7 381 2 88 19 5 96 3 82 22 7 80 4 374 21 9 365

4 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 cwater   fr 1000 (0) ms

The value of fr is 1 (without units). Its standard uncertainty u(fr) is found as follows:

sr u( f r )  (0) cwater

sr can be found from the repeatability data (Table 1). The combined standard uncertainty of cwater is found as follows:

2 2 2 2  u(V )   u(T )   u(m )   u( f )   t   t   s   r  (0) uc (cwater )  cwater             Vt   Tt   ms   fr 

2. Measurement data for uncertainty calculation using the ISO GUM Modelling approach

Table 3. Measurement data.

Input quantity or Unit Value Uncertainty Type of Comments component uncertainty

Vt ml 0.720

u(Vt, cal) ml 0.004 Rectangular

u(Vt, temp) ml 0.002 Standard

u(Vt, rep) ml – Taken into account by fr

Tt mg/ml 4.9987 0.05 Standard ms g 9.7734

u(ms, drift) g 0.0002 Rectangular

u(ms, nonlin) g 0.0002 Rectangular

u(ms, rep) g – Taken into account by fr fr Unitless 1 Uncertainty to be found from repeatability data

Table 3 presents the data relevant for the particular measurement for which the uncertainty is calculated.

5 3. Equations for uncertainty calculation using in-house validation data

The notations are the same is used in the Nordtest handbook for uncertainty evaluation1.

2 2 uc  u(Rw )  u(bias) (0)

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:

2 2 (0) u(bias)  RMSbias  u(Cref )

RMSbias is the root mean of bias. u(Cref) is the uncertainty of the reference value against which the bias is found.

(bias )2 RMS   i (0) bias 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 ) )2 u(Cref )   i (0) n 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.

si u(Cref )i  (0) ni

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)