pls-toolbox-preprocessing

To ensure consistent preprocessing across datasets, you must calibrate preprocessing parameters from your training data and store them in a structured object. You then apply this calibrated pipeline to new data samples, which enforces identical scaling, normalization, and derivative transformations for accurate multivariate model validation.

Yes, you can undo preprocessing transformations on PLS_Toolbox models where mathematically possible. The undo workflow allows you to recover original data after applying calibrated preprocessing, which is essential for validating multivariate models and comparing transformed spectral data against initial measurements.

Yes, PLS_Toolbox supports multi-step scaling and normalization workflows through its preprocess function. You can chain multiple preprocessing operations together, such as combining scaling, centering, and derivative calculations, to create a comprehensive pipeline that integrates directly with EVRIModel objects.

The best way to prepare spectral data for PLS models is to construct a structured preprocessing pipeline using the PLS_Toolbox preprocess function. Start with preprocess('default', 'mean center'), calibrate the pipeline on your training set, and apply the consistent transformations to all subsequent data.

You need to calibrate a preprocessing pipeline when you want to enforce consistent scaling, normalization, and derivative calculations across different datasets. Calibrating extracts parameters from your training data and stores them in a structured object, ensuring new data undergoes identical transformations for reliable PLS_Toolbox model performance.