Deconvolution of Mass Spectra Through Particle Swarm Optimization: An Industrial Experience

Electron Ionization (EI) libraries enable fast identification of unknowns from their mass spectra, but classical dot–product–style search falters when the observed spectrum is a superposition and chromatographic support is unavailable. We present a deconvolution workflow that relies solely on the EI–MS spectrum, requiring no external information. The method was developed with an industrial laboratory partner and evaluated on synthetic and real spectra. The method employs a two–stage strategy. First, a diagnostic–aware whitelist screens the library by rewarding agreement on strong, informative ions while penalizing unsupported intensity and maximizing over small integer m/z shifts to absorb minor misalignment. Second, a PSO-driven greedy builder assembles a sparse mixture from the shortlist, allowing a bounded per–component power stretch to accommodate modest intensity variability, followed by a brief joint refinement. We also introduce a similarity ordering of the library (cosine on log normalized spectra with optimal–leaf ordering) so that local neighbor scans probe look–alike references without altering reported indices. The fitness combines global NMSE, peak–weighted NMSE, and a spectral–angle term to emphasize diagnostic ions while preserving overall shape. On instrument–like synthetic mixtures and preliminary checks on laboratory and field EI spectra, the approach yields high–recall reconstructions. While over–selection persists in our tests, this is a reasonable trade-off given the size and redundancy of EI libraries, and preferable to missing true constituents, with settings that are straightforward to apply in practice.