Signals and Noise
- Page ID
- 77460
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Defining Signal and Noise
All analytical data sets contain two components: signal and noise
Signal
- This is the part of the data that contains information about the chemical species of interest (i.e. analyte).
- Signals are often proportional to the analyte mass or analyte concentration
- Beer-Lambert Law in spectroscopy where the absorbance, A, is proportional to concentration, C.
\[\mathrm{A = εbC}\]
There are other significant relationships between signal and analyte concentration:
- The Nernst equation where a measured potential (E) is logarithmically related to the activity of an analyte (ax)
\[\mathrm{E = E^\circ ± \dfrac{RT}{nF} \ln a_x}\]
- Competitive immunoassays (e.g. ELISA) where labeled (analyte spike) and unlabeled analyte molecules (unknown analyte) compete for antibody binding sites
\[\mathrm{A = kN_{binding\: sites} = \dfrac{C_{labeled}} {C_{(labeled+unlabeled)}}}\]
- Beer-Lambert Law in spectroscopy where the absorbance, A, is proportional to concentration, C.
Noise
- This is the part of the data that contains extraneous information.
- Noise originates from various sources in an analytical measurement system, such as:
- Detectors
- Photon Sources
- Environmental Factors
Therefore, characterizing the magnitude of the noise (N) is often a difficult task and may or may not be independent of signal strength (S).
- A more detailed discussion on specific relationships between signal and noise may be obtained by clicking here and reading Section 3.