{
  "url": "https://openlegislation.uk/eu-decision-2002-657/annex/chapter-3.1/division-3.1.3.1",
  "json_url": "https://openlegislation.uk/eu-decision-2002-657/annex/chapter-3.1/division-3.1.3.1.json",
  "legislation": "eudn/2002/657",
  "legislation_title": "Commission Decision of 12 August 2002 implementing Council Directive 96/23/EC concerning the performance of analytical methods and the interpretation of results (notified under document number C(2002) 3044) (Text with EEA relevance) (2002/657/EC)",
  "legislation_url": "https://openlegislation.uk/eu-decision-2002-657",
  "path": "annex/chapter/3.1/division/3.1.3.1",
  "kind": "division",
  "number": "3.1.3.1",
  "heading": "Experimental plan",
  "text": "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.\nExample:\nseveral analytes can be investigated simultaneously with the analytical method being validated,\ntwo 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,\nthe selected factors are to be varied on two levels (indicated as either + or -).\nTable 13\nExamples for factors considered important for a validation procedure\nGender of the animal\n(factor 1)\nBreed\n(factor 2)\nTransport conditions\n(factor 3)\nStoring conditions\n(factor 4)\nFreshness of the sample\n(factor 5)\nFattening conditions\n(factor 6)\nDifferent operators with different experience\n(factor 7).\nTable 14\nPossible experimental plan for the above example\nSpecies\nFactor 1\nFactor 2\nFactor 3\nFactor 4\nFactor 5\nFactor 6\nFactor 7\nSample No\nA\n+\n+\n+\n+\n–\n+\n–\n1\nA\n+\n+\n–\n–\n+\n–\n–\n2\nA\n+\n–\n+\n–\n–\n–\n+\n3\nA\n+\n–\n–\n+\n+\n+\n+\n4\nA\n–\n+\n+\n–\n+\n+\n+\n5\nA\n–\n+\n–\n+\n–\n–\n+\n6\nA\n–\n–\n+\n+\n+\n–\n–\n7\nA\n–\n–\n–\n–\n–\n+\n–\n8\nB\n+\n+\n+\n+\n+\n–\n+\n9\nB\n+\n+\n–\n–\n–\n+\n+\n10\nB\n+\n–\n+\n–\n+\n+\n–\n11\nB\n+\n–\n–\n+\n–\n–\n–\n12\nB\n–\n+\n+\n–\n–\n–\n–\n13\nB\n–\n+\n–\n+\n+\n+\n–\n14\nB\n–\n–\n+\n+\n–\n+\n+\n15\nB\n–\n–\n–\n–\n+\n–\n+\n16\nAs 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.\nFrom these 80 measurement results it is possible to compute (13)(14).\nRecovery\nrepeatability per concentration level (sir),\nwithin-laboratory reproducibility per concentration level (sir),\ndecision limit (CCɑ),\ndetection capability (CCβ),\npower curve (β-error rate versus concentration (see 3.1.3.2),\nruggedness of major changes; ruggedness to minor changes can be determined according to paragraph 3.1.1.3,\n16 sample-related calibration curves,\none overall calibration curve,\nprediction interval of the overall calibration curve,\nmatrix-induced deviations (smat),\nrun-induced deviations (srun),\neffect of the individual factors on the measurement results.\nThese 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.",
  "extent": "E+W+S+N.I.",
  "status": null,
  "version": "current",
  "annotations": [],
  "outstanding_effects": [],
  "provenance": {
    "source": "legislation.gov.uk",
    "source_url": "https://www.legislation.gov.uk/eudn/2002/657/annex/chapter/3.1/division/3.1.3.1",
    "retrieved_at": "2026-11-01T15:18:31.211349+00:00"
  },
  "licence": "Open Government Licence v3.0",
  "licence_url": "https://www.nationalarchives.gov.uk/doc/open-government-licence/version/3/",
  "attribution": "Contains public sector information licensed under the Open Government Licence v3.0. Source: legislation.gov.uk, The National Archives."
}