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AMEBaS: Automatic Midline Extraction and Background Subtraction of Ratiometric Fluorescence Time-Lapses of Polarized Single Cells
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Mixture models for two-dimensional baseline correction, applied to artifact elimination in time-resolved spectroscopy
Johan J de Rooi1, Olivier Devos, Michel Sliwa
1Department of Bioinformatics, Erasmus Medical Center, Rotterdam, The Netherlands.
Analytica Chimica Acta
|March 26, 2013
Summary
This study introduces a new algorithm for baseline correction and artifact removal in analytical chemistry, enhancing data quality for spectroscopic and chromatographic analyses. The method effectively eliminates strong artifact signals, improving the accuracy of scientific measurements.
Area of Science:
- Analytical Chemistry
- Spectroscopy
- Chemometrics
Background:
- Baseline correction and artifact removal are crucial pre-processing steps in analytical chemistry.
- Existing methods may not adequately address complex artifact signals in multidimensional data.
Purpose of the Study:
- To develop and present a novel algorithm for robust baseline correction and artifact removal.
- To apply the algorithm to femtosecond time-resolved spectroscopy data for solvent artifact elimination.
Main Methods:
- A mixture model combined with penalized regression using tensor product P-splines.
- Data points are classified into baseline noise or artifact components via mixture model weights.
- The method is designed for anisotropic smoothing of two-way data.
Main Results:
- The proposed algorithm effectively corrects baselines and removes artifacts.
- Demonstrated successful application in eliminating strong solvent artifact signals in time-resolved spectroscopy.
- The method shows broad applicability for two-way data like 2D gel electrophoresis and chromatography.
Conclusions:
- The mixture model and penalized regression approach offers a powerful tool for data pre-processing in analytical chemistry.
- This method enhances the reliability of data analysis, particularly in spectroscopy and chromatography.
- The algorithm provides a robust solution for eliminating challenging artifact signals.
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