SparseEB-gMCR: A Generative Solver for Extreme Sparse Components with Application to Contamination Removal in GC-MS

Le résumé fourni par la source

Analytical chemistry instruments provide physically meaningful signals for elucidating analyte composition, and those with high mass or spectral resolution generate signals sparse enough for direct interpretation against chemical libraries. enerative multivariate curve resolution (gMCR) models a sample as the linear superposition of a few components drawn from a learned pool, and its energy-based solver (EB-gMCR) recovers the pool and the component count without being told the count. However, extreme sparsity in instruments such as GC-MS or 1H-NMR leaves the component profiles themselves sparse, and a dense learnable profile cannot represent an exact zero. To address this, a static support gate was introduced that applies the EB-select mechanism a second time, to the coordinates of each profile rather than to the components of each sample. The result, SparseEB-gMCR, reparameterizes the gMCR pool rather than changing the model, and the sparsity that motivates the extension makes components easier to tell apart rather than harder. On synthetic data, SparseEB-gMCR recovered the component count and reconstructed sparse mixtures as accurately as dense-component EB-gMCR, with the same graceful scaling in the number of components. It was then applied to real GC-MS chromatograms for unsupervised contamination removal, where siloxane-related pollution signals were eliminated and compound identification became more reliable. Removal reuses a pool learned from clean spectra alone, and rests on one requirement: that no combination of clean components can imitate the contamination, which is stronger than the two sets of components merely being different. With this sparse extension, the EB-gMCR family becomes applicable to wider ranges of real-world chemical datasets, providing a general mathematical framework for signal unmixing and contamination elimination in analytical chemistry.

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