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Simultaneous spectrum fitting and baseline correction using sparse representation


Sparse representation has been applied in many domains, such as signal processing, image processing and machine learning. In this paper, a redundant dictionary with each column composed of a Voight-like lineshape is constructed to represent the pure spectrum of the sample. With smooth prior of the baseline and sparse representation coefficient for pure spectrum, a method simultaneously fitting the pure spectrum and baseline is proposed. Since the pure spectrum is nonnegative, the representation coefficients are also made to be nonnegative. Then through alternating optimization, a surrogate function based algorithm is used to obtain the sparse coefficients. Finally, we adopt one simulated data set and two real data sets to evaluate our method. The results of quantitative analysis show that our method successfully estimates the baseline and pure spectrum and are superior compared to other baseline correction and preprocessing methods.

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Publication details

The article was received on 24 Oct 2016, accepted on 05 May 2017 and first published on 10 May 2017

Article type: Paper
DOI: 10.1039/C6AN02341J
Citation: Analyst, 2017, Accepted Manuscript
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    Simultaneous spectrum fitting and baseline correction using sparse representation

    Q. Han, Q. Xie, S. Peng and B. Guo, Analyst, 2017, Accepted Manuscript , DOI: 10.1039/C6AN02341J

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