Signals¶

• 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¶

Accuracy¶

• 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)

Sensitivity¶

• 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

Selectivity¶

• 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.