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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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by
Akunna Francess UJUAGU,
Ziteng WANG,
Shin-ichi MORITA
Published
in Analytical Sciences
by Japan Society for Analytical Chemistry.
2020 Volume 36, Issue 5, p511-514
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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