Rime samples characterization and comparison using classical and fuzzy principal components analysis
- 45 Downloads
The main objective of this paper is to introduce principal component analysis and two robust fuzzy principal component algorithms as useful tools in characterizing and comparing rime samples collected in different locations in Poland (2004–2007). The efficiency of the applied procedures was illustrated on a data set containing 108 rime samples and concentration of anions, cations, HCHO, as well as pH and conductivity. The fuzzy principal component algorithms achieved better results mainly because they are more compressible than classical PCA and very robust to outliers. For example, a three component model, fuzzy principal component analysis-first component (FPCA-1) accounts for 62.37% of the total variance and fuzzy principal component analysis-orthogonal (FPCA-o) 90.11%; PCA accounts only for 58.30%. The first two principal components explain 51.41% of the total variance in the case of FPCA-1 and 79.59% in the case of FPCA-o as compared to only 47.55% for PCA. As a direct consequence, PCA showed only a partial differentiation of rime samples onto the plane or in the space described by different combination of two or three principal components, whereas a much sharper differentiation of the samples, regarding their origin and location, is observed when FPCAs are applied.
KeywordsRime Anions Cations Chemometric
Unable to display preview. Download preview PDF.
- S. Baranowski, J. Liebersbach, J. Glaciol. 19, 489 (1978)Google Scholar
- H. Lorenc, Atlas klimatu Polski (IMGW, Warszawa 2005) (In Polish)Google Scholar
- M. Sobik, Alpex Regional Bulletin. Swiss Meteorological Institute 17, 26 (1991)Google Scholar
- M. Błaś, M. Sobik, Natural and Human Impact on Pollutant Deposition in Mountain Ecosystems with the Sudetes as an Example. In: J.L. Pyka, M. Dubicka, A. Szczepankiewicz-Szmyrka, M. Sobik, M. Błaś (eds), Man and Climate in the 20th Century. Acta UW rat 2542, Stu Geo., 75, 411 (2003)Google Scholar
- J.W. Einax, H.W. Zwanziger, S. Geiß, Chemometrics in Environmental Analysis (John Wiley & Sons Ltd, Chichester 1997)Google Scholar
- M. Otto, Chemometrics: Statistics and Computer Application in Analytical Chemistry (Wiley-VCH, Weinheim 1999)Google Scholar
- K.H. Esbensen, Multivariate Data Analysis in Practice (CAMO, Oslo 2002)Google Scholar
- R.G. Brereton, Applied Chemometrics for Scientist (John Wiley & Sons, Chichester 2007)Google Scholar
- J.C. Bezdek, Pattern Recognition with Fuzzy Objective Function Algorithms. (Plenum Press, New York, 1987)Google Scholar
- C. Sârbu, J. AOAC Int., 83, 1463 (2000)Google Scholar
- C. Sârbu, H.F. Pop, Fuzzy Soft-Computing Methods and Their Applications in Chemistry. In: K.B. Lipkowitz, R. Larter, T.R. Cundari (Eds.), Reviews in Computational Chemistry, Wiley-VCH, 20, 249 (2004)Google Scholar