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Comparative Analysis of a Principal Component Analysis-Based and an Artificial Neural Network-Based Method for
Roberto C Carvajal1, Luis E Arias2, Hugo O Garces3
1Department of Electrical Engineering, Universidad de Concepción, Concepción, Chile Center for Optics and Photonics, Universidad de Concepción, Concepción, Chile.
Applied Spectroscopy
|February 27, 2016
Summary
A new principal component analysis (PCA) method effectively removes spectral baselines, outperforming artificial neural networks (ANN) in performance and simplicity for spectral data analysis.
Area of Science:
- Spectroscopy
- Chemometrics
- Data Analysis
Background:
- Baseline drift is a common artifact in spectral data, complicating quantitative analysis.
- Existing methods for baseline removal include parametric and non-parametric approaches.
Purpose of the Study:
- To introduce and evaluate a non-parametric principal component analysis (PCA) method for spectral baseline removal.
- To compare the PCA-based method against a parametric artificial neural network (ANN) approach.
Main Methods:
- A non-parametric method utilizing PCA on a spectral learning matrix to estimate and remove baseline features.
- A parametric method employing an ANN for spectral baseline filtering.
- Evaluation using a synthetic spectral database and a real-world flame radiation dataset.
Main Results:
- The PCA-based method demonstrated superior performance compared to the ANN method.
- The PCA approach was also found to be simpler to implement and use.
- Performance metrics included correlation coefficient, chi-square, and goodness-of-fit.
Conclusions:
- The PCA-based method is a highly effective and simpler alternative for continuous spectral baseline removal.
- This approach offers significant advantages over traditional ANN-based methods for spectral data processing.
