Comparison of Raman Spectra Estimation Algorithms

Abstract

Raman spectroscopy is a powerful and effective technique for analyzing and identifying the chemical composition of a substance. Two types of Raman spectra estimation algorithms exist: supervised and unsupervised. In this paper, we perform a comparative analysis of five supervised algorithms for estimating Raman spectra. We describe a realistic measurement model for a dispersive Raman measurement device and observe that the measurement error variances vary significantly with bin index. Monte Carlo analyses with simulated measurements are used to calculate the bias root mean square error, and computational time for each algorithm. Our analyses show that it is important to use correct measurement weights and enforce the nonnegative constraint in parameter estimation.

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Document Details

Document Type
Technical Report
Publication Date
Jul 01, 2009
Accession Number
ADA533582

Entities

People

  • Aaron Lanterman
  • Andy Register
  • Barry Drake
  • Dale Blair
  • Darren Emge
  • Haesun Park
  • Mahendra Mallick
  • Phil West
  • Ryan Palkki

Organizations

  • Edgewood Chemical Biological Center

Tags

Communities of Interest

  • Autonomy
  • Counter WMD
  • Materials and Manufacturing Processes
  • Sensors

DTIC Thesaurus Topics

  • Accuracy
  • Algorithms
  • Charge Coupled Devices
  • Chemical Composition
  • Computations
  • Detection
  • Detectors
  • Diffraction
  • Measurement
  • Monte Carlo Method
  • Raman Scattering
  • Raman Spectra
  • Raman Spectroscopy
  • Scattering
  • Simulations
  • Spectra
  • Spectroscopy

Readers

  • Materials Science and Engineering.
  • Neural Network Machine Learning.
  • Statistical inference.