Method for Removing Spectral Contaminants to Improve Analysis of Raman Imaging Data.

ABSTRACT: The spectral contaminants are inevitable during micro-Raman measurements. A key challenge is how to remove them from the original imaging data, since they can distort further results of data analysis. Here, we propose a method named "automatic pre-processing method for Raman imaging data set (APRI)", which includes the adaptive iteratively reweighted penalized least-squares (airPLS) algorithm and the principal component analysis (PCA). It eliminates the baseline drifts and cosmic spikes by using the spectral features themselves. The utility of APRI is illustrated by removing the spectral contaminants from a Raman imaging data set of a wood sample. In addition, APRI is computationally efficient, conceptually simple and potential to be extended to other methods of spectroscopy, such as infra

SUBMITTER: Zhang X 

PROVIDER: S-EPMC5215229 | biostudies-literature | 2017 Jan

REPOSITORIES: biostudies-literature