Raman peak-fit uncertainty

ID: raman-peak-fit-uncertainty

Raman peak-fit uncertainty characterizes the uncertainty of a spectral-band position estimated by fitting a model to a Raman spectrum. It accompanies a Raman peak-position map: a colour difference between pixels need not establish a physical shift if the estimated positions are insufficiently precise. In Raman mapping, the instrument collects spectra at successive sample positions, and fitting extracts band parameters such as position and width.[ref]
For an isolated band, a least squares fit might estimate the center, height, width and background of a Gaussian function. The uncertainty assigned to its center is conditional on the chosen model and description of spectral noise. Repeating the complete acquisition and fit at one unchanged location provides another assessment: if independent center estimates have standard deviation , the estimated standard uncertainty of their sample mean is . This reduction applies to independent variation; it does not eliminate an offset shared by every acquisition.[ref]
For an original numerical example, model two fitted positions as
where represents a shared uncertain additive correction from measurement calibration, while are independent fitting errors, also independent of . Suppose their standard uncertainties are and . Each position then has combined standard uncertainty . Their difference cancels the same , giving
Here denotes standard uncertainty, so the difference has uncertainty about . The covariance term is essential: treating the two positions as entirely independent would instead give about . A common additive correction cancels only insofar as it really is shared; instrument drift between measurements or a scale error need not cancel.[ref]
For a hypothetical stress model with an exactly known illustrative , this difference uncertainty contributes to the standard uncertainty of stress. Uncertainty in would require its own contribution. Raman peak-model bias, overlapping bands, an inadequate background and laser-induced sample changes can invalidate an otherwise small fit error. Retaining spectra and repeating fits with physically defensible models therefore matters alongside quoting a numerical uncertainty.

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