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

Analytical and Bioanalytical Chemistry volume 400, pages 625–634 (2011)Cite this article

Abstract

Discrete wavelet transform (DWT) provides a well-established means for spectral denoising and baseline elimination to enhance resolution and improve the performance of calibration and classification models. However, the limitation of a fixed filter bank can prevent the optimal application of conventional DWT for the multiresolution analysis of spectra of arbitrarily varying noise and background. This paper presents a novel methodology based on an improved, second-generation adaptive wavelet transform (AWT) algorithm. This AWT methodology uses a spectrally adapted lifting scheme to generate an infinite basis of wavelet filters from a single conventional wavelet, and then finds the optimal one. Such pretreatment combined with a multivariate calibration approach such as partial least squares can greatly enhance the utility of Raman spectroscopy for quantitative analysis. The present work demonstrates this methodology using two dispersive Raman spectral data sets, incorporating lactic acid and melamine in pure water and in milk solutions. The results indicate that AWT can separate spectral background and noise from signals of interest more efficiently than conventional DWT, thus improving the effectiveness of Raman spectroscopy for quantitative analysis and classification.

The Raman spectrum of trace melamine in water through an adaptive wavelet prism

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Acknowledgements

This work was supported by British Columbia Innovation Council and Natural Sciences and Engineering Research Council of Canada.

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Affiliations

  1. State Key Laboratory of Precision Measuring Technology and Instruments, Tianjin University, Tianjin, 300072, China

    Da Chen

  2. Department of Chemistry, University of British Columbia, Vancouver, BC, V6T 1Z1, Canada

    Zhiwen Chen & Edward Grant

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  1. Da Chen
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  2. Zhiwen Chen
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  3. Edward Grant
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Correspondence to Edward Grant.

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Chen, D., Chen, Z. & Grant, E. Adaptive wavelet transform suppresses background and noise for quantitative analysis by Raman spectrometry. Anal Bioanal Chem 400, 625–634 (2011). https://doi.org/10.1007/s00216-011-4761-5

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  • DOI: https://doi.org/10.1007/s00216-011-4761-5

Keywords

  • Adaptive wavelet transform
  • Multivariate quantitative analysis
  • Wavelet regression
  • Baseline compensation
  • Denoising
  • Raman spectroscopy