Unmasking Airborne Threats: Guided-Transformers for Portable Aerosol Mass Spectrometry

• URL: http://arxiv.org/abs/2511.17446v1
• Date: Fri, 21 Nov 2025 17:45:00 GMT
• Title: Unmasking Airborne Threats: Guided-Transformers for Portable Aerosol Mass Spectrometry
• Authors: Kyle M. Regan, Michael McLoughlin, Wayne A. Bryden, Gonzalo R. Arce,
• Abstract summary: Matrix Assisted Laser Desorption/Ionization Mass Spectrometry (MALDI-MS) is a cornerstone in biomolecular analysis, offering precise identification of pathogens through unique mass spectral signatures.<n>Yet, its reliance on labor-intensive sample preparation and multi-shot spectral averaging restricts its use to laboratory settings, rendering it impractical for real-time environmental monitoring.<n>These limitations are especially pronounced in emerging aerosol MALDI-MS systems, where autonomous sampling generates noisy spectra for unknown aerosol analytes.<n>We propose the Mass Spectral Dictionary-Guided Transformer (MS-DGFormer), a data-driven framework that redefines spectral
• Score: 2.743898388459522
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Matrix Assisted Laser Desorption/Ionization Mass Spectrometry (MALDI-MS) is a cornerstone in biomolecular analysis, offering precise identification of pathogens through unique mass spectral signatures. Yet, its reliance on labor-intensive sample preparation and multi-shot spectral averaging restricts its use to laboratory settings, rendering it impractical for real-time environmental monitoring. These limitations are especially pronounced in emerging aerosol MALDI-MS systems, where autonomous sampling generates noisy spectra for unknown aerosol analytes, requiring single-shot detection for effective analysis. Addressing these challenges, we propose the Mass Spectral Dictionary-Guided Transformer (MS-DGFormer): a data-driven framework that redefines spectral analysis by directly processing raw, minimally prepared mass spectral data. MS-DGFormer leverages a transformer architecture, designed to capture the long-range dependencies inherent in these time-series spectra. To enhance feature extraction, we introduce a novel dictionary encoder that integrates denoised spectral information derived from Singular Value Decomposition (SVD), enabling the model to discern critical biomolecular patterns from single-shot spectra with robust performance. This innovation provides a system to achieve superior pathogen identification from aerosol samples, facilitating autonomous, real-time analysis in field conditions. By eliminating the need for extensive preprocessing, our method unlocks the potential for portable, deployable MALDI-MS platforms, revolutionizing environmental pathogen detection and rapid response to biological threats.

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