OASIS用新损失函数实现多谱图自动分析，无需人工干预。
OASIS: A Deep Learning Framework for Universal Spectroscopic Analysis Driven by Novel Loss Functions
- 基于定制合成数据和任务专用损失函数，实现跨技术谱图处理。
- 在拉曼、紫外-可见、荧光实验数据上准确完成去噪、基线校正与峰参数提取。
- 适合高通量筛选、在线监测等需自动化分析的场景。
光谱数据在众多科学与工程领域快速增长，亟需自动化处理方法。我们提出OASIS（Omni-purpose Analysis of Spectra via Intelligent Systems），一个技术无关的全自动光谱分析机器学习框架，可独立完成去噪、基线校正及峰位置、强度、半高宽（FWHM）等参数的完整提取，无需人工介入。OASIS的通用性源于在精心设计的合成数据集上训练的模型，该数据集融合了多种光谱技术特征。关键在于开发了任务特异的创新损失函数，如用于峰定位的邻域峰响应（ViPeR），使模型在保持紧凑的同时达到高精度，且已在拉曼、紫外-可见、荧光等实验数据上验证。OASIS在原位实验、高通量优化和在线监测中展现出显著应用潜力。本研究强调优化损失函数是实现高性能模型的资源高效策略。
原文摘要 · Abstract (English)
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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