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Automatic Background Removal and Correction of Systematic Error Caused by Noise Expecting Bio-Raman Big Data Analysis

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Abstract

Spectral pretreatments, such as background removal from Raman big data, are crucial to have a smooth link to advanced spectral analysis. Recently, we developed an automated background removal method, where we considered the shortest length of a spectrum by changing the scaling factor of the background spectrum. Here, we propose a practical way to correct the systematic error caused by noise from measurements. This correction has been realized to be more effective and accurate for automatic background removal.

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Authors and Affiliations

  1. Graduate School of Science, Tohoku University, 6-3 Aramaki-Aza-Aoba, Aoba, Sendai, 980-8578, Japan

    Akunna Francess Ujuagu, Ziteng Wang & Shin-ichi Morita

Authors
  1. Akunna Francess Ujuagu
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  2. Ziteng Wang
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  3. Shin-ichi Morita
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Correspondence to Shin-ichi Morita.

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Ujuagu, A.F., Wang, Z. & Morita, Si. Automatic Background Removal and Correction of Systematic Error Caused by Noise Expecting Bio-Raman Big Data Analysis. ANAL. SCI. 36, 511–514 (2020). https://doi.org/10.2116/analsci.20C005

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  • DOI: https://doi.org/10.2116/analsci.20C005

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