Methods#

Add random variation in your data to simulate different real-world scenarios.

Preprocess your data to remove remove noise and highlight the chemical information.

Select the most chemically relevant features to improve the performance of your model.

Detect outliers from your data to improve diagnostics and reliability of the results.

Create publication-quality visualizations for spectral data and model diagnostics.

Interactively explore and diagnose your PCA and PLS models with comprehensive visualizations.