Automatic background removal and correction of systematic error caused by noise expecting bio-raman big data analysis

Akunna Francess Ujuagu, Ziteng Wang, Shin ichi Morita

Research output: Contribution to journalArticlepeer-review

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.

Original languageEnglish
Pages (from-to)511-514
Number of pages4
Journalanalytical sciences
Volume36
Issue number5
DOIs
Publication statusPublished - 2020

Keywords

  • Analysis
  • Background removal
  • Big data
  • Bio-Raman
  • Correction
  • Noise
  • Raman
  • Subtraction
  • Systematic error

ASJC Scopus subject areas

  • Analytical Chemistry

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