Background Removal and Spectrum Identification for SERS Data

Implements a new approach 'SABARSI' described in Wang et al., "A Statistical Approach of Background Removal and Spectrum Identification for SERS Data" (Unpublished). Sabarsi forms a pipeline for SERS (surface-enhanced Raman scattering) data analysis including background removal, signal detection, signal integration, and cross-experiment comparison. The background removal algorithm, the very first step of SERS data analysis, takes into account the change of background shape.


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Reference manual

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install.packages("sabarsi")

0.1.0 by Li Jun, 16 days ago


Browse source code at https://github.com/cran/sabarsi


Authors: Li Jun [cre] , Wang Chuanqi [aut]


Documentation:   PDF Manual  


GPL-3 license


Imports stats

Suggests knitr, rmarkdown


See at CRAN