Automated Hyperspectral 2D/3D Raman Analysis Using the Learner-Predictor Strategy: Machine Learning-Based Inline...

Area of Science:

• Spectroscopy
• Data Analysis
• Machine Learning

Background:

• Hyperspectral Raman analysis presents challenges in detecting multiple spectral signatures due to large data volumes and complex pipelines.
• Cosmic noise elimination is a critical bottleneck hindering autonomous Raman analytics.

Purpose of the Study:

• To develop an automated hyperspectral Raman analysis framework for rapid molecular variation fingerprinting.
• To address the challenge of cosmic noise elimination and enable autonomous Raman data processing.

Main Methods:

• Implemented a learner-predictor strategy for autonomous Raman analytics.
• Utilized the spectrum angle mapper (SAM) technique for cosmic noise eradication.
• Developed a neural network (LNN) model for predicting abundance maps from learned spectral features.

Main Results:

• The framework autonomously eliminates baseline and cosmic noise, extracting key spectral signatures and abundance maps.
• The learner-predictor strategy obviates the need for human inference, enabling real-time prediction of abundance maps.
• The system is suitable for 2D, 3D, and 4D hyperspectral Raman techniques, requiring only a personal computer.

Conclusions:

• The proposed machine learning framework provides an intuitive and efficient solution for hyperspectral Raman analysis.
• It facilitates autonomous quality assurance/quality control (QA/QC) processes for industrial applications.
• The approach significantly simplifies the end-to-end Raman analytics pipeline.