Adaptive wavelet transform suppresses background and noise for quantitative analysis by Raman spectrometry

Area of Science:

• Analytical Chemistry
• Spectroscopy
• Signal Processing

Background:

• Discrete Wavelet Transform (DWT) is used for spectral denoising and baseline elimination.
• Fixed filter banks in conventional DWT limit optimal multiresolution analysis for complex spectral noise.
• Improved spectral resolution and model performance are crucial for quantitative analysis.

Purpose of the Study:

• To introduce a novel Adaptive Wavelet Transform (AWT) methodology for spectral pretreatment.
• To enhance the quantitative analysis and classification capabilities of Raman spectroscopy.
• To overcome the limitations of conventional DWT in handling arbitrarily varying noise and background.

Main Methods:

• Developed a second-generation AWT algorithm utilizing a spectrally adapted lifting scheme.
• Generated an infinite basis of wavelet filters from a single conventional wavelet.
• Applied AWT pretreatment followed by partial least squares (PLS) multivariate calibration.
• Validated the methodology on Raman spectral data of lactic acid and melamine in various solutions.

Main Results:

• AWT demonstrated superior efficiency in separating spectral background and noise compared to conventional DWT.
• The methodology significantly improved the quantitative analysis of spectral data.
• Enhanced performance in classification models was observed using AWT-pretreated data.
• Effective application in complex matrices like milk solutions was confirmed.

Conclusions:

• The proposed AWT methodology offers a more efficient approach to spectral denoising and baseline correction.
• AWT enhances the utility of Raman spectroscopy for accurate quantitative analysis and classification.
• This adaptive approach provides a powerful tool for spectral data pretreatment in various analytical applications.