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  • A signal is an output reading from an instrument that represents a sample
  • All signals are either Gaussian or Lorentzian in distribution, meaning they are all perfectly symmetrical
    • If not symmetrical, the signal will be made of constituent symmetrical contributors
    • The process of reversing this is called deconvolution and is a computational process.
  • Signals are always accompanied by noise, the general background uncertainty of the universe.
  • The ratio of the signal:noise can be used as a measure of statistical usefulness.


Performance Characteristics


  • Arise from determinate (non random) errors
  • Can be:
    • Instrumental - the instrument is not functioning correctly. (operating temp, calibration error, etc.)
    • Personal - Judgement errors (reading at the wrong angle, subjective determination of endpoint)
    • Methodical - a result of poor experiment design (slow reactions, instability of reagents, concentration change from volatilisation)



  • How well a technique is capable of detecting a change in signal
  • How much does the signal change for a change in the measured variable
  • Can be depicted by the slope of the calibration curve
    • Dependent on the scale of both axis


Detection Limit

  • The smallest amount of analyte that can be reliably read
  • Often considered to be \(3\times\)SNR

Quantisation limit

  • Is the limit of what the instrument can be used to make quantitative determinations.
  • Considered to be \(10\times\)SNR

Linearity limit

  • As samples are taken of increasing concentration, often, if the readings continue, a linear trend will disappear


Dynamic Range

  • The range between the quantisation limit and linearity limit


  • How capable is the technique of detecting the analyte without excessive interference
  • This can be accounted for with a method blank, however all components cannot always be negated completely
    • A selectivity coefficient can be produced to represent how much of the signal is actually from the analyte, compared to the rest of the matrix.