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2006 | 4 | 1 | 68-80
Article title

An advanced multivariate statistical approach to study coastal sediment data

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The present paper deals with the application of classical and fuzzy principal components analysis to a large data set from coastal sediment analysis. Altogether 126 sampling sites from the Atlantic Coast of the USA are considered and at each site 16 chemical parameters are measured. It is found that four latent factors are responsible for the data structure (“natural”, “anthropogenic”, “bioorganic”, and “organic anthropogenic”). Additionally, estimating the scatter plots for factor scores revealed the similarity between the sampling sites. Geographical and urban factors are found to contribute to the sediment chemical composition. It is shown that the use of fuzzy PCA helps for better data interpretation especially in case of outliers.
Physical description
1 - 3 - 2006
1 - 3 - 2006
  • [1] J. Martin and M. Whitfield: The Significance of the River Input of Chemical Elements to the Ocean, Plenum Press, New York, 1983.
  • [2] National Status and Trends Program: Monitoring Site Descriptions (1984-1990) for the National Mussel Watch and Benthic Surveillance Projects, NOAA Office of Oceanography and Marine Assessment, Rockville, MD, 1998.
  • [3] Second Summary of Data on Chemical Contaminants in Sediments from the NS&TP, NOAA, Technical Memorandum 59, NOS OMA, Rockville, MD, 1991.
  • [4] K. Daskalakis and Th. O'Connor: “Distribution of Chemical Concentrations in US Coastal and Estuarine Sediment”, Marine Environ. Res., Vol. 40, (1995), pp. 381–398.[Crossref]
  • [5] P. Hanson, D. Evans, D. Colby and V. Zdanowicz: “Assessment of Elemental Contamination in Estuarine and Coastal Environments based on Geochemical and Statistical Modeling of Sediments”, Marine Environ. Res., Vol. 36, (1993), pp. 237–266.[Crossref]
  • [6] A. Cantillo and Th. O'Connor: “Trace Elements Contaminants in Sediments from the NOAA National Status and Trends Programme Compared to Data from Throughout the World”, Chem. Ecol., Vol. 7, (1992), pp. 31–50.
  • [7] I. Stanimirova, S. Tsakovski and V. Simeonov: “Multivariate Statistical Analysis of Coastal Sediment Data”, Fres. J. Anal. Chem., Vol. 365, (1999), pp. 489–493.[Crossref]
  • [8] V. Simeonov, S. Tsakovski and D. L. Massart: “Multivariate Statistical Modeling of Coastal Sediment Data”, Toxicolog. Environ. Chem., Vol. 72, (1999), pp. 81–92.[Crossref]
  • [9] V. Simeonov, I. Stanimirova and S. Tsakovski: “Multivariate Statistical Interpretation of Coastal Sediment Monitoring Data”, Fres. J. Anal. Chem., Vol. 370, (2001), pp. 719–722.[Crossref]
  • [10] K. Esbensen, S. Schoenkopf and T. Midtgaard: Multivariate Analysis in Practice, CAMO AS, Trondheim, 1994.
  • [11] B. Vandeginste, D. L. Massart, L. Buydens, S. De Jong, P. Lewi and J. Smeyers-Verbeke: Handbook of Chemometrics and Qualimetrics, Elsevier, Amsterdam, 1998.
  • [12] C. Sârbu and H.F. Pop: “Fuzzy Soft-Computing Methods and Their Applications in Chemistry”, In: K.B. Lipkowitz, D.B. Boyd and T.R. Cundari (Eds.): Rev. Comput. Chem., Chapter 5, Wiley-VCH, 2004, pp. 249-332.
  • [13] T. Cundari, C. Sârbu and H.F. Pop: “Robust Fuzzy Principal Component Analysis (FPCA). A Comparative Study Concerning Interaction of Carbon-Hydrogen Bonds with Molybdenum-Oxo Bonds”, J. Chem. Inf. Computer Sci., Vol. 42, (2002), pp. 310–321.
  • [14] H.F. Pop and C. Sârbu: “A New Fuzzy Regression Algorithm”, Anal. Chem., Vol. 68, (1996), pp. 771–780.[Crossref]
  • [15] C. Sârbu: “Use of Fuzzy Regression for Calibration in TLC-Densitometry”, J. AOAC Internat., Vol. 83, (2000), pp. 1463–1467.
  • [16] K.Y. Lee: “Local Fuzzy PCA based GMM with Dimension Reduction on Speaker Identification”, Pattern Recognition Lett., Vol. 25, (2004), pp. 1811–1817.[Crossref]
  • [17] P. Piraino, E. Parente and P.L.H. McSweeney: “Processing of Chromatographic Data for Chemometric Analysis of Peptide Profiles from Cheese Extracts: A Novel Approach”, J. Agricultur. Food Chem., Vol. 52, (2004), pp. 6904–6911.[Crossref]
  • [18] C. Sârbu and H. F. Pop: “Principal Components Analysis versus Fuzzy Principal Components Analysis: A Case Study: The Quality of Danube Water (1895-1996)”, Talanta, Vol. 65, (2005), pp. 1215–1220.[Crossref]
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