Preprocessing Options
April 30, 2015

When viewing the scans of a data collection, the preprocessing options allow you to view your data collection spectrum with different algorithms applied. These can help you identify outliers, inaccurate scans and reveal possible relationships in your data.


Within the graphs SCiO Lab Mobile presents:


X is the wavelength represented in nm (nanometers).
Y is dependent on the state of the toggle buttons you select.
  • Nothing is selected: Raw reflectance spectrum
  • Processed (only): Assumes Beer-Lambert model is valid, and transforms the measured signal to be linear with concentration by doing a log transform and adjusting the result for noise and deviations from the model. You can learn more about Beer-Lambert here:
  • Normalized (only): Performs normalization of the signal. This is meant to compensate for changing measurement conditions (e.g. varied scanning distances) that typically occur from sample to sample. Y axis still means reflectance but in normalized units instead of raw reflectance.
  • Both Processed and Normalized: First assumes Beet-Lambert model (Processed) and then normalizes the results to compensate for differences in the optical path between samples. This is useful, for example, when there is variation in the thickness of the samples.
Typically, different models and types of samples will require different pre-processing methods. You should both choose the pre-processing method to match your experimental setup and optimize the performance of your model. If you planned and gathered your data correctly, these efforts will coincide.
Tip: More preprocessing options are available when using SCiO Lab (web). Use SCiO Lab Mobile for initial review and SCiO Lab (web) for deep analysis of your data collections. 

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