Automatic removal of binary background components expecting Raman big data and its application to human hair imaging
Year: 2020
Type: article
Abstract: We developed an automated method for removing binary background components from observed Raman spectra by tuning the scaling factors to seek the minimum lengths of the subtracted spectra. This method is effective, especially for large data including imaging data. For application, 400 Raman imaging spectra of a sliced cross section of a strand of gray human hair, fixed by glue on glass, were subjected to the proposed method by removing the glass and glue information. After the binary background removal, principal component analysis successfully detected small but important signals of tryptophan, which is peculiar to the hair cortex.
Source: Applied Physics Express
Institutions: Tohoku University, Milbon Co., Ltd. (Japan)
Language: English
Subfield: Biophysics
Domain: Life Sciences
Open Access status: bronze
OpenAlex ID: W3000910118