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Walter, D.* ; Helbig, M.* ; Weydanz, B.* ; Voltmer, D.* ; Bonfiglio, J.J.* ; Marr, C. ; Großkopf, T.*

Learning to see peaks: Attention-based feature extraction for automated chromatographic peak detection.

ACS Omega 11, 32946–32954 (2026)
Publ. Version/Full Text Research data DOI
Open Access Gold
Creative Commons Lizenzvertrag
Reliable peak detection remains a bottleneck in size-exclusion chromatography (SEC) as overlapping signals, drifting baselines, and analyst variability limit reproducibility. As SEC is a routine release and comparability assay and its interpretation depends on peak morphology and context, machine learning methods are well-suited to improve reproducibility at scale. We present the Peak Feature Extractor 1 (PFE-1), a one-dimensional encoder-only transformer trained on millions of synthetic chromatograms generated by a simulator statistically calibrated to routine SEC data from antibodies and related large-molecule species. PFE-1 outputs probabilistic region and event predictions that are aggregated through a transparent rule-based procedure into interpretable peak boxes. We evaluate PFE-1 on synthetic benchmarks and on a curated real SEC benchmark, reporting window-level precision/recall/F1 and box-level agreement via an intensity-weighted box loss aligned with routine process annotations. Across these evaluations, PFE-1 outperforms convolutional and derivative-based baselines, with the largest gains observed under more challenging overlap and morphology conditions. On synthetic data, PFE-1 achieves substantially higher box-level agreement than both baselines; on the curated real SEC benchmark, it likewise achieves the strongest box-level agreement while requiring no sample-specific inputs (e.g., expected peak windows). We provide a reproducible and extensible SEC-specific framework for chromatographic peak detection that supports a more consistent peak interpretation in routine analytical workflows.
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Publication type Article: Journal article
Document type Scientific Article
Keywords Pattern Recognition (psychology) ; Probabilistic Logic ; Reproducibility ; Feature (linguistics) ; Bottleneck ; Synthetic Data ; Normalization (sociology)
ISSN (print) / ISBN 2470-1343
e-ISSN 2470-1343
Journal ACS Omega
Quellenangaben Volume: 11, Issue: 22, Pages: 32946–32954 Article Number: , Supplement: ,
Publisher American Chemical Society (ACS)
Publishing Place 1155 16th St, Nw, Washington, Dc 20036 Usa
Reviewing status Peer reviewed
Institute(s) Institute of AI for Health (AIH)
Grants Roche