Hi Jack,
We are very excited to see our developer community growing every day and we are thrilled that you are a part of it.
Just to remind you of the current mathematical algorithms as part of our pre-processing method options:
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.
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.
Both (log)R))” and Normalized: Similar to Processed and Normalized, uses a more aggressive form of Processed. Adds more noise, but in some cases may be the only way to create a good model.
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.
An expert mode of SCiO lab which enable users to apply different mathematical algorithms such as: Log, Derivative and SNV, will be released in the near future.
Attached is a screenshot of this new feature.
Ayelet,
The ConsumerPhysics Team
