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Related Concept Videos

Raman Spectroscopy Instrumentation: Overview01:26

Raman Spectroscopy Instrumentation: Overview

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A conventional Raman spectrophotometer includes a laser source, a sample holding system, a wavelength selector, and a detector.
The monochromatic laser source, typically using visible or near-infrared radiation, generates a highly focused beam of light. This light interacts with the molecules of the sample, scattering some of the light. Liquid and gaseous samples are usually tested in ordinary glass capillaries, while solids can be analyzed as powders packed in capillaries or as potassium...
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Raman Spectroscopy: Overview01:20

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The underlying principle of Raman spectroscopy is based on the interaction between light and matter, specifically molecules' inelastic scattering of photons. When a monochromatic beam of light, typically from a laser source, interacts with a sample, most scattered light has the same frequency as the incident light. This is known as Rayleigh scattering.
However, a small fraction of the scattered light exhibits a frequency shift due to the exchange of energy between the incident photons and...
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Automated Hyperspectral 2D/3D Raman Analysis Using the Learner-Predictor Strategy: Machine Learning-Based Inline

Ankur Baliyan1, Hideto Imai1, Akansha Dager2

  • 1NISSAN ARC Ltd., 1-Natsushima-cho, Yokosuka 236-0061, Japan.

Analytical Chemistry
|December 21, 2021
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Summary
This summary is machine-generated.

This study introduces an automated hyperspectral Raman analysis framework using a learner-predictor strategy to efficiently remove cosmic noise and extract spectral signatures from 2D/3D Raman data for autonomous quality control.

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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.