OASIS: A Deep Learning Framework for Universal Spectroscopic Analysis Driven by Novel Loss Functions
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.
Cite
@article{arxiv.2509.11499,
title = {OASIS: A Deep Learning Framework for Universal Spectroscopic Analysis Driven by Novel Loss Functions},
author = {Chris Young and Juejing Liu and Marie L. Mortensen and Yifu Feng and Elizabeth Li and Zheming Wang and Xiaofeng Guo and Kevin M. Rosso and Xin Zhang},
journal= {arXiv preprint arXiv:2509.11499},
year = {2025}
}