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

研究成果: Article査読

2 被引用数 (Scopus)

抄録

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.

本文言語English
ページ(範囲)511-514
ページ数4
ジャーナルanalytical sciences
36
5
DOI
出版ステータスPublished - 2020

ASJC Scopus subject areas

  • 分析化学

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