Abstract
Identifying characteristic bands, those exhibiting the most distinct features (i.e., minimal correlation among themselves), is a critical step in two-dimensional correlation spectroscopy (2D-COS) analysis. This process is essential for establishing effective correlation filters to simplify congested spectral datasets. Historically, such bands were selected using subjective methods, primarily the visual inspection of correlation cross-peaks. We now propose a more systematic and objective procedure based on the sequential multiplication of horizontal slices from a 2D discrimination spectrum. This unsupervised, automatic method is potentially integrable into model-free 2D-COS analyses, making it compatible with automated, machine-based interpretation.
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