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Signal Processing techniques for Raman spectroscopy

ICA and digital filter analysis applied to Raman spectra of calcite-dolomite mixtures

Pablo Hernández Ferreirós

on 2 December 2013

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Transcript of Signal Processing techniques for Raman spectroscopy

Calcite and dolomite
Raman effect
Rock forming: Limestone, marble
Studied by CV Raman
Inelastic scattering of light
Characterizes molecules
Signal processing techniques
in Raman spectroscopy
Case study: Calcite-dolomite mixtures
Pablo Hernández-Ferreirós

Independent Component Analysis
Digital Filter Analysis

Raman microscope
Background subtraction
Source separation
Library search
Spectra mixing for FastICA
Weighted sum
2 concentration profile models
1. Peaks concentration profile
49x49 Raman spectra sampled on a rectangular area.
2. Random concentration profile
10x10 Raman spectra with random concentrations
Raman Spectra mixing
Spectra mixing for filter analysis
Independent Component Analysis (ICA)
Model follows the equation: x = As
Raman sources:
Non-Gaussian, non overlapping.
Linear mixing.
IC physically interpretable.
Algorithm: FastICA for MATLAB
1. Prewhitening: PCA
2. Rotation: ICA
a) Initial b) Mixed c) Whitened
a) Principal components
b) Independent components
Calcite-dolomite demixing with ICA
1. Peaks concentration
2. Random concentration
Dot product: 0.99999
RMSE: 0.0001
500 profiles
Both calcite 10% and dolomite 10%
Digital filter analysis
Time domain signal: Inverse FFT
Raman spectrum modelled as filter
Objective: Pole-zero representation
Digital filter models
1. AR model
2. ARMA model
Frequency response
ICA very good qualitative and quantitative separator.
Better than available methods.
Exact concentration profile estimator.
Raman spectra analysis as filter is possible.
Very interesting properties.
Unexplored field.
Need to confirm data with real samples.
Results should not diverge by much.
Thank You!
Full transcript