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

2025-09-14

AI Summary

In the context of rapidly increasing multi-source spectral data, existing methods suffer from strong technical dependency, excessive manual intervention, and poor generalizability. Method: This paper proposes a technology-agnostic, fully automated spectral analysis framework. It employs a lightweight deep learning model trained on synthetically generated spectra, incorporating a novel task-specific loss function (ViPeR) that jointly optimizes denoising, baseline correction, and extraction of peak parameters—including position, intensity, and full width at half maximum (FWHM). The training strategy ensures both physical interpretability and broad spectral generalizability. Contribution/Results: Experimental validation across Raman, UV-vis, and fluorescence spectra demonstrates high-accuracy, zero-intervention analysis. The framework achieves >10× speedup in processing time and improves quantitative accuracy, with mean absolute error <3%. It is robustly applicable to in situ detection, high-throughput screening, and real-time online monitoring.

Technology Category

• Machine Learning: Evaluation and Analysis
• Intelligent Robots: Multimodal Perception & Sensor Fusion
• Data Mining & Knowledge Management: Anomaly/Outlier Detection

Application Category

• Web Mining and Content Analysis: Robustness and generalizability of Web mining methods
• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics
• Economics, Online Markets and Human Computation: Data quality aspects of human-annotated datasets

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.

Problem

• Automated processing of spectroscopic data across fields
• Technique-independent spectral analysis without human intervention
• Retrieving peak parameters via novel loss functions

Innovation

• Deep learning framework with novel loss functions
• Synthetic dataset training for universal spectroscopy analysis
• Automated peak parameter retrieval without human intervention