A Spectral Matching Algorithm Based on the Wasserstein Metric

Through visual inspection, scientists can easily judge the similarity of pairs of spectra from different sources, whether they are experimental measurements, spectra libraries, or spectra calculations. Spectral similarity is recognized when the peak patterns appear similar, even if the peak positions are shifted or the peak amplitudes are changed. However, it is desirable to have an objective spectra distance measure that can be calculated in terms of a numerical distance number. This work introduces a move‐and‐scale spectral matching algorithm based on the Wasserstein metric as a distance measure between pairs of spectra. The focus is on situations where only part of a spectrum (e.g., the spectral signature of a functional group in a molecule) is matched to a specific window of a full‐spectrum range with a more complex peak pattern. The spectral matching algorithm is used to assign calculated spectra by density functional theory (DFT) to pure component spectra based on the peak group analysis (PGA) of FTIR spectroscopic datasets from transition metal‐catalyzed carbonylation reactions.