Explore a MATLAB toolbox for automated spectral preprocessing selection in this 53-minute video. Learn about a novel approach to filtering preprocessing algorithms based on raw data properties, eliminating the need for trial-and-error methods. Discover how the toolbox quantifies noise and scatter effects to determine optimal preprocessing algorithms for spectral correction. Understand the steps of data analysis, importance of preprocessing, and classification of algorithms. Delve into smoothing techniques, scatter effect corrections, and automation processes. Evaluate performance using decision trees and examine real-world examples. Gain insights into the JScore metric and its application in preprocessing selection. Benefit from this time-saving tool that helps create optimal PLS models, surpassing results achieved by non-expert analysts through traditional methods.