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arXiv · 2509.11499

OASIS: A Deep Learning Framework for Universal Spectroscopic Analysis Driven by Novel Loss Functions

Chris Young·Juejing Liu·Marie L. Mortensen·Yifu Feng·Elizabeth Li·Zheming Wang·Xiaofeng Guo·Kevin M. Rosso·Xin Zhang
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Abstract

The proliferation of spectroscopic data across various scientific and engineering fields necessitates automated processing. We introduce OASIS (Omni-purpose Analysis of Spectra via Intelligent Systems), a machine learning (ML) framework for technique-independent, automated spectral analysis, encompassing denoising, baseline correction, and comprehensive peak parameter (location, intensity, FWHM) retrieval without human intervention. OASIS achieves its versatility through models trained on a strategically designed synthetic dataset incorporating features from numerous spectroscopy techniques. Critically, the development of innovative, task-specific loss functions-such as the vicinity peak response (ViPeR) for peak localization-enabled the creation of compact yet highly accurate models from this dataset, validated with experimental data from Raman, UV-vis, and fluorescence spectroscopy. OASIS demonstrates significant potential for applications including in situ experiments, high-throughput optimization, and online monitoring. This study underscores the optimization of the loss function as a key resource-efficient strategy to develop high-performance ML models.

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Chris Young, Juejing Liu, Marie L. Mortensen, Yifu Feng, Elizabeth Li, Zheming Wang, Xiaofeng Guo, Kevin M. Rosso, Xin Zhang. 2025-09-15. OASIS: A Deep Learning Framework for Universal Spectroscopic Analysis Driven by Novel Loss Functions. https://arxiv.org/abs/2509.11499

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