Why is processed data useful?
April 30, 2015
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.