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

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)”...

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Bibliographic Details
Published in:Scientific reports 2017-01, Vol.7 (1), p.39891-39891, Article 39891
Main Authors: Zhang, Xun, Chen, Sheng, Ling, Zhe, Zhou, Xia, Ding, Da-Yong, Kim, Yoon Soo, Xu, Feng
Format: Article
Language:English
Subjects:
631/1647/245
631/1647/527/1821
Contaminants
Data processing
Datasets
Humanities and Social Sciences
Infrared spectroscopy
multidisciplinary
NMR
Nuclear magnetic resonance
Principal components analysis
Science
Spectral analysis
X-ray diffraction
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Summary: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 infrared (IR), nuclear magnetic resonance (NMR), X-Ray Diffraction (XRD). With the help of our approach, a typical spectral analysis can be performed by a non-specialist user to obtain useful information from a spectroscopic imaging data set.
ISSN:2045-2322
2045-2322
DOI:10.1038/srep39891