P 03 ISSRNS 2012: Abstracts / Synchrotron Radiation in Natural Science Vol. 11, No 1 – 2 (2012)
ANALYSIS OF SYNCHROTRON RADIATION INDUCED X-RAY EMISSION SPECTRA WITH R ENVIRONMENT
K. Banas1∗, A.M. Banas1, M. Gajda2, W.M. Kwiatek3, B. Pawlicki4, and M.B.H. Breese1,5
1Singapore Synchrotron Light Source, National University of Singapore, 5 Research Link, 117603 Singapore
2Department of Histology, Jagiellonian University Medical College, Kopernika 7, 31–034 Krak´ow, Poland
3Institute of Nuclear Physics PAN, Radzikowskiego 152, 31–342 Krak´ow, Poland
4Gabriel Narutowicz Hospital, Pr¸adnicka 37, 31–202 Krak´ow, Poland
5Physics Department, National University of Singapore, 2 Science Drive 3, 117542 Singapore Keywords: synchrotron radiation, XRF, fluorescence spectroscopy, R platform, multivariate analysis
∗e-mail : slskb@nus.edu.sg
Life sciences have seen a huge increase in the amount and complexity of data being collected with every experiment. Scientists today are faced with increasingly difficult task to extract vital informa- tion from the vast amount of numbers. Software used for this purpose should be sufficiently power- ful and flexible to handle large and complex data sets. On the other hand it should allow the user to exactly follow what is being calculated – black-box type of software should be avoided.
R Platform [1] for statistical analysis nicely fits these requirements. With its rapidly expanding user community is quickly becoming the most important tool in statistical analysis of data in biology, geol- ogy, genetics, physics and chemistry to name just few of them. The most important feature of R is the package system, allowing users to address spe- cific problems with dedicated package and even for more advanced users to contribute software for their own fields. At this moment there are 3705 packages at CRAN — The Comprehensive R Archive Net- work [2] (state for 15.03.2012). R is available free of charge for most of contemporary operating sys- tems including Windows, MacOS and wide variety of UNIX based platforms.
In this presentation application of the R plat- form for the spectral preprocessing as well as univariate and multivariate statistical analysis is shown. One of the advantages of using R is high
quality graphics that could be produced in vari- ous formats: bitmap files (jpg and png) and vector graphics (postscript and pdf).
Analysis of the spectral data obtained from the experiment usually includes: baseline correction, normalization, sometimes subtracting common for every spectrum component, removing artifacts like spikes or glitches, offset correction etc. All these procedures could be implemented in R. One of the packages for this kind of analysis is hyperSpec [3].
In order to reduce the amount of variables in the spectral datasets multivariate approach like cluster analysis and principal component analysis [4] is ex- tremely useful. In this poster results of univari- ate analysis (concentration distribution maps for selected elements) are compared with multivariate way of spectral data treatment.
Acknowledgments: This work was partially per- formed under NUS Core Support C-380-003-003-001.
References
[1] R platform http://www.r-project.org/.
[2] CRAN http://cran.r-project.org/.
[3] hyperSpec package http://hyperspec.r-forge.r- project.org/.
[4] W. Hardle and L. Simar, Applied Multivariate Sta- tistical Analysis (Springer, 2007).
Figure 1 : Typical SRIXE spectrum of bi- ological sample (a), spatial distribution of zinc (b), dendrogram calculated for spec- tral distance (c), spatial distribution of four clusters (d).
82
