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
2020 Analytical Sciences  
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.
doi:10.2116/analsci.20c005 pmid:32307345 fatcat:5tm5xqlolbg23g6yuto26npz3u