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Project Summary

Analyzed 5 months ago. based on code collected 5 months ago.

1. Introduction

An intelligence background-correction algorithm for highly fluorescent sample in Raman spectroscopy has been developed with peak detection and width estimation by CWT wavelet and background fitting by penalized least squares. The programming language is R(

2. Installation

Firstly, you must download and install R 2.8.1 from the urls as follows:

for linux:
for windows:
Then, download the baselineWavelet package from this project download pages.

for linux:

for windows:
Finally,install the downloaded packages from local zip or tar.gz file.

To start running this algorithm, load the baselineWavelet package through "library(baselineWavelet)" in the R commandline windows, try "?baselineWavelet" in the R commandline windows to open the documents.

3. What's new

What's new in newer version:

1. From version 2.0 to 3.0: Rewirte the WhittakerSmooth function, don't use the cholskey decomposition any more.

2. From version 1.0 - 2.0: Two functions, say baselineCorrectionCWT() and WhittakerSmoother(), in the baselineWavelet package were modified (add a parameter) so that one could easily perform first, second or even higher differences penalties by adjusting the parameter for the purpose.

4. Correction example This is a correction example:


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