SCiO’s estimation algorithms assume a linear dependency between sample attributes and the algorithmic inputs. However, SCiO measures the reflection spectra, which does not have that linear dependence but is rather exponential, according to the Beer-Lambert model.
Our “processed” algorithm transforms the scanned spectra to a form that is linear with concentration by performing a log transform and taking the first derivative (with respect to wavelength) and removing the average. Removing the average of the results helps to correct discrepancies from the Beer-Lambert model.


Note: The Beer-Lambert model removes gain, but SNV is better at it.

Processing takes the log (natural algorithm), then the first derivative and then removes the average. It removes gain, but more importantly , transforms the equation to be linear with concentration (assuming the Beer-Lambert model applies).