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Understanding why results are as good or bad as they are ('2Cy'), may:

  • be the first step towards improvement or optimization,
  • provide evidence that target limits are too stringent for that analyte and measurement method,
  • lead to tighter confidence/prediction/tolerance intervals and lower detection limits, hence a wider scope for specific hardware in food safety, pharmaceutical analysis, anti-doping research, environmental analysis, forensic science, metrology, &c.,
  • enable one to operate closer to the target value in process analysis,
  • reduce development times, because various aspects such as data pre-treatment, sample selection, factor selection and outlier detection can be consistently handled, and
  • help to develop self-calibrating instruments.


It stands to reason that these benefits add value to an instrument.

One may also find inspiration from the following authoritative editorial (dowload, icon_pdf.gif=81 kB):

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