Near-Infrared Spectroscopy Analysis: Extracting Relevant Information to Build Reliable Models

In near-infrared spectroscopy analysis, effectively extracting relevant information from complex data to establish reliable models requires training sets with strong representativeness. Current sample selection methodologies include Random Sampling (RS), Kennard-Stone (KS), and Sample Set Partitioning based on joint X-Y distances (SPXY), each with distinct algorithmic implementations for optimal data partitioning.