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Noise remaining after scaled Raman subtraction is the random uncertainty left when a surface contribution's mean has been cancelled. For two measured spectral channels $X,Y$ and fixed scale $k$, scaled subtraction of two spatially offset Raman spectra uses $Z=Y-kX$. Its exact variance isThe covariance term describes the actual acquisition relationship; numerical subtraction does not itself make the inputs correlated or independent. General measurement propagation retains such terms.^[ref]

A focused extension allows an uncertain subtraction scale $K$, independent of the pair $(X,Y)$, with mean $k$ and variance ${\sigma }_k^2$. Expanding the product's moments givesThe final term includes uncertainty in the reference scale, not just shot noise in the two spectra.

For an original surface-only example, let independent Poisson photoelectron counts have means ${\mu }_X=20000$ and ${\mu }_Y=6000$, with $k=0.30$. The mean residual is zero. A perfectly known scale gives variance $6000+0.30^2(20000)=7800$ and standard deviation $88.3$. If instead ${\sigma }_k=0.005$, the variance becomes $7800+0.005^2(20000^2+20000)=17800.5$, giving standard deviation $133.4$ in the scaled count units.

If $K$ is estimated from these same counts, the independence assumption needs replacement by the appropriate joint model. A scale shared across wavelengths can also produce correlated spectral residuals from a shared subtraction scale, leaving a structured band-like error rather than independent noise in each bin. The original SORS study demonstrates changing layer contributions with collection offset; the moment calculation here is an independent noise model for a simplified subtraction.^[ref]

Projection of shared-scale uncertainty onto a spectral contrast determines which measured spectral features are sensitive to an uncertain common subtraction coefficient. In correlated spectral residuals from a shared subtraction scale, a fixed reference shape $\mathbf{x}$ and scale error $\delta k$ contribute $-\mathbf{x}\delta k$ to the spectrum. For a linear contrast $c={\mathbf{a}}^T\mathbf{z}$,The contrast is insensitive to this particular error when ${\mathbf{a}}^T\mathbf{x}=0$. This is an exact cancellation for a fixed reference shape; it does not require the scale error to have a Gaussian distribution.

Suppose the desired spectral signal has known shape $\mathbf{s}$ and unknown amplitude $\theta$, so that $\mathbf{z}=\theta \mathbf{s}-\delta k\mathbf{x}+\mathbf{ϵ}$. To retain its amplitude while rejecting the scale error, requireFor an original two-channel example, take $\mathbf{x}=(1,1{)}^T$ and $\mathbf{s}=(1,0{)}^T$. The difference $c=z_1-z_2$ has unit signal response and cancels any common scale error. If the two independent reading noises each have standard deviation $3$ in the chosen units, the difference has standard deviation $\sqrt{18}\approx 4.24$. Using channel 1 alone would have reading-noise standard deviation $3$ but would retain the unknown common contribution. Rejection has a noise cost even when the nuisance cancellation is exact.

If $\mathbf{s}$ is proportional to $\mathbf{x}$, the two requirements are incompatible: cancelling that reference direction also cancels the signal. In more channels, a minimum-variance spectral contrast orthogonal to a reference shape can balance detector noise among the remaining directions. The relevant variance is ${\mathbf{a}}^{T}C\mathbf{a}$, including correlations.^[ref] Uncertain reference shape or scale estimates correlated with the measured spectra require their own covariance contributions.