Automatic removal of binary background components expecting Raman big data and its application to human hair imaging

Akunna Francess Ujuagu, Momoko Furuta, Takakazu Nakabayashi, Len Ito, Shin Ichi Morita

Research output: Contribution to journalArticlepeer-review

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.

Original languageEnglish
Article number036501
JournalApplied Physics Express
Volume13
Issue number3
DOIs
Publication statusPublished - 2020 Mar 1

ASJC Scopus subject areas

  • Engineering(all)
  • Physics and Astronomy(all)

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