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2013 | 11 | 4 | 610-618
Tytuł artykułu

Selection of mineralised methods to analyse different types of matrices. Applying the Box-Cox transformation to chemometrics study the coexistence of heavy metals in natural samples

Treść / Zawartość
Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
Chemometric methods are mostly used to optimise technological processes and analytical procedures. Applying chemometric methods in environmental tests may reveal relationships among chemical elements in biomes. Cluster analysis and principal component analysis (PCA) are very helpful for detecting relationships among studied parameters. However, large amounts of data may have a negative effect on this analysis and can lead to misinterpretation of the results. This situation was observed when the samples, taken from several places in the Silesian Province, were used to test the relationship between heavy metals contained in various environmental matrices. Samples were collected from a small area and were characterised by a single biome (pine forest) because direct interpretation of PCA and CA was insufficient to correctly describe such data. The solution to this problem was the use of the Box-Cox transformation, which is a rapid method to normalise input data. [...] The application of chemometric tools enabled the relationships between sampling sites (industrialised and non-industrialised) to be examined and was very helpful in illustrating the relationship between the methodologies of plant preparation samples. Furthermore, the results may indicate the need for further data analysis. The tools described in this paper can be useful for choosing the optimal mineralisation method according to the type of test matrix.
Wydawca

Czasopismo
Rocznik
Tom
11
Numer
4
Strony
610-618
Opis fizyczny
Daty
wydano
2013-04-01
online
2013-01-23
Twórcy
  • Department of Analytical Chemistry, Faculty of Chemistry, Silesian University of Technology, 44-100, Gliwice, Poland
Bibliografia
  • [1] M. Żelazny, A. Astel, A. Wolanina, S. Małek, Environmental Pollution 159, 1048 (2011) http://dx.doi.org/10.1016/j.envpol.2010.11.021[Crossref]
  • [2] T. Togkalidou, H. Tung, Y. Sun, A. Andrews, R.D. Braatz, Organic Process Research & Development 6, 317 (2002) http://dx.doi.org/10.1021/op015516x[Crossref]
  • [3] G.E.P. Box, D.R. Cox, J. Roy. Statist. Soc. B 26, 211 (1964)
  • [4] STATISTICA - data analysis software system, version 9 (StatSoft, Inc. STATISTICA - data analysis software system, version 9 (StatSoft, Inc., USA, 2009)
  • [5] M. Sajdak, C. Pieszko, Eur. J. Chem. 10(5), 1696 (2012)
  • [6] M. Sajdak, Eur. J. Chem. 11(2), 151 (2013)
  • [7] M. Sajdak, O. Piotrowski, Eur. J. Chem. 11(2), 259 (2013)
  • [8] M. Tobiszewski, J. Namieśnik, Anal. Bioanal. Chem. 399, 3565 (2011) http://dx.doi.org/10.1007/s00216-011-4676-1[Crossref]
  • [9] P. McCullagh, J.A Nelder, Generalized Linear Models, 2nd edition (Chapman & Hall, London, 1989)
  • [10] G. Romanik, E. Gilgenast, A. Przyjazny, M. Kamiński, Dear Sirce 70, 253 (2007)
  • [11] P. Konieczka, J. Namieśnik, Chem. Anal. (Warsaw) 53, 785 (2008)
  • [12] C.H. Lochmuller (Ed.), Chemical Laboratory - a Practical Approach (CRC/Taylor & Francis, Boca Raton, USA, 2009) 131–216
Typ dokumentu
Bibliografia
Identyfikatory
Identyfikator YADDA
bwmeta1.element.-psjd-doi-10_2478_s11532-012-0196-x
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