Division 3.1.3.1 Experimental plan
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An experimental plan has to be designed depending on the number of different species and different factors under investigation. Hence, the first step of the entire validation procedure shall consider the sample populations that will be analysed in the laboratory in the future in order to select the most important species and those factors which may influence the measurement results. Subsequently, the concentration range shall be chosen in a purpose-adapted way according to the level of interest.
Example:
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several analytes can be investigated simultaneously with the analytical method being validated,
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two variations of the leading factor have been identified (A and B). Leading factors form the basis on which the factor levels are combined. These leading factors may include factors such as species or matrix. In this example the leading factor was varied on two levels, i.e. two different species (species A and B) were considered. In general, it is possible to vary the leading factors on more than two levels, which only increase the number of analyses to be performed,
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the selected factors are to be varied on two levels (indicated as either + or -).
Table 13
Examples for factors considered important for a validation procedure
| Gender of the animal | (factor 1) |
| Breed | (factor 2) |
| Transport conditions | (factor 3) |
| Storing conditions | (factor 4) |
| Freshness of the sample | (factor 5) |
| Fattening conditions | (factor 6) |
| Different operators with different experience | (factor 7). |
Table 14
Possible experimental plan for the above example
| Species | Factor 1 | Factor 2 | Factor 3 | Factor 4 | Factor 5 | Factor 6 | Factor 7 | Sample No |
|---|---|---|---|---|---|---|---|---|
| A | + | + | + | + | – | + | – | 1 |
| A | + | + | – | – | + | – | – | 2 |
| A | + | – | + | – | – | – | + | 3 |
| A | + | – | – | + | + | + | + | 4 |
| A | – | + | + | – | + | + | + | 5 |
| A | – | + | – | + | – | – | + | 6 |
| A | – | – | + | + | + | – | – | 7 |
| A | – | – | – | – | – | + | – | 8 |
| B | + | + | + | + | + | – | + | 9 |
| B | + | + | – | – | – | + | + | 10 |
| B | + | – | + | – | + | + | – | 11 |
| B | + | – | – | + | – | – | – | 12 |
| B | – | + | + | – | – | – | – | 13 |
| B | – | + | – | + | + | + | – | 14 |
| B | – | – | + | + | – | + | + | 15 |
| B | – | – | – | – | + | – | + | 16 |
As each sample (each factor level combination) has to be spiked with four different concentrations around the level of interest, and one blank sample shall be analysed for each level, 5 x 16 = 80 analyses have to be performed for the entire validation experiment.
From these 80 measurement results it is possible to compute (13)(14).
Recovery
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repeatability per concentration level (sir),
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within-laboratory reproducibility per concentration level (sir),
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decision limit (CCɑ),
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detection capability (CCβ),
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power curve (β-error rate versus concentration (see 3.1.3.2),
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ruggedness of major changes; ruggedness to minor changes can be determined according to paragraph 3.1.1.3,
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16 sample-related calibration curves,
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one overall calibration curve,
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prediction interval of the overall calibration curve,
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matrix-induced deviations (smat),
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run-induced deviations (srun),
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effect of the individual factors on the measurement results.
These performance characteristics allow the comprehensive evaluation of the performance level of the method, since not only the influence of the individual factors is investigated, but also the relevant combinations of these factors. With the help of this experiment design it is possible to decide if one or the other of the selected factors shall be excluded from the overall calibration curve, because it significantly deviates from the standard deviations of the other factors.