Resval. Practical tool for the validation of an analytical method and the quantification of the uncertainty of measurement.

Transcription

1 Workshop... Resval. Practical tool for the validation of an analytical method and the quantification of the uncertainty of measurement. Henk Herbold Marco Blokland Saskia Sterk

2 General topics. *What s the main purpose of validation? *What are the benefits of a validation software program? 2

3 In short words: why validation? The main purpose of validation of analytical methods is: to get an up-to-date insight in the analytical possibilities of a method. (CCα and CCß) to measure the uncertainty of measurement. 3

4 MEASUREMENT UNCERTAINTY Definition: A parameter associated with the result of a measurement, that characterizes the dispersion of the values that could reasonably be attributed to the measurand (The measurand will be the concentration of an analyte) 4

5 Uncertainty sources. In practice, the uncertainty of the result may arise from many possible sources: Sampling Matrix effects and interferences Uncertainties of masses or volumetric equipment. Reference values etc. 5

6 Quantifying the combined standard deviation Example: ß-Boldenone in samples of faeces. Concentration of the measurement is 25 ng (X) Standard deviation is 1.5 ng (STD) In the blank is found 5 ng (STD = 0.5 ng) Weight sample is 1 g. (STD = 0.05 g) Calculate the combined STD. Formula: S = Std 12 + Std 22 Std n 2 6

7 Quantifying the combined standard deviation 7

8 Estimate of the uncertainty. In practice: The overall standard uncertainty U, is estimated by combining the variances of reproducibility and the influence of the matrix. U =k. S S 22.+ S N 2 S = standard deviation K=2 U = expanded uncertainty (95%) (The uncertainty is quantified in terms of the combined standard deviation). 8

9 Estimate of the uncertainty. Coverage factor K (EURACHEM/CITAC GUIDE: Quantifying Uncertainty in Analytical Measurement) Student s t for 95 % confidence (2-tailed) Degrees of freedom t U =k. S S 22.+ S N 2 9

10 Calculation of Decision limit (CCα)and Detection capability (CCß). Definition: The decision limit (CCα) is the lowest concentration level of the analyte that can be detected in a sample with a chance of 1% of a false positive decision. The detection capability (CCß) is the smallest content of the analyte that can be detected in a sample with a chance of 5% of a false negative decision. (conform EC/2002/657 and ISO17025). 10

11 How to calculate CCα and CCß? The calculation of CCα and CCß is based on the linear regression line, formula: Y = M * X + B0 CCα is the corresponding concentration (x) at the y- intercept plus 2.33 times the STD CCα= (2.33*STD)/M CCß is the corresponding concentration at the decision limit (x) plus 1.64 times the STD CCß=CCα+(1.64*STD)/M 11

12 CCα: decision limit 1.64 SD CCβ: detection capability 2.33 SD assumption: SD=constant 12

13 Quantifying the CCα. The corresponding concentration at the y- intercept (Bo) plus 2.33 times the standard deviation of the reproducibility equals the decision limit (CCα). (conform EC/2002/657). Formula: Y = M *X + Bo or X=(Y-Bo)/M M = (slope) Bo = (intercept) STD = CCα = ((2.33* )-1.518)/1.930 = 0.36

14 Quantifying the CCß. The corresponding concentration at the decision limit (CCα) plus 1.64 times the standard of the reproducibility equals the detection capability (CCß). (conform EC/2002/657). CCß = ((2.33*0.295+Bo) *STD)-Bo)/M = 0.61 M = (slope) Bo = (intercept) STD = 0.295

15 Conclusion. The decision limit (CCα) is This means: the lowest concentration level that can be detected with a chance of 1% of a false positive decision is The detection capability (CCß) is This means: The smallest content that can be detected with a chance of 5 % of a false negative decision is

16 Demonstration Demonstration of the software program which is developed at the RIVM, for the validation of analytical methods. 16

17 Our strategy MRPLParameter Day 1 Day 2 Day 3 Day CCα and CCß Repeatability Reproducibility Repeatability Reproducibility Repeatability Reproducibility CCα and CCß CCα and CCß Specificity - Ruggedness Number of analyses experiments

18 Comparing the conventional method versus the in-house validation approach. The number of experiments Parameters Conventional In-house Decision limit CCα Detect. capability CCß Repeatability (With-in) Reproducibility Accuracy mean Specificity Ruggedness 6 analyses, 3 levels No additional analyses 6 analyses 3 days, 3 levels 6 analyses 3 days, 3 levels No additional analyses 20 blanc/20 fortified M(P)RL No additional analyses 166 experiments 3 additional analyses 3 days.(0,2,5 times M(P)RL). 6 analyses 3 days, 3 levels No additional analyses No additional analyses 10 blanc/10 fortified M(P)RL No additional analyses 83 experiments 18

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20 ResVal Day 1 20

21 ResVal Day 2 21

22 ResVal Day 3 22

23 ResVal Day 4 23

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28 CRL Homepage 28