Skip to main content
Log in

Chemometric analysis of soil pollution data using the Tucker N-way method

  • Original Paper
  • Published:
Analytical and Bioanalytical Chemistry Aims and scope Submit manuscript

Abstract

N-way methods, particularly the Tucker method, are often the methods of choice when analyzing data sets arranged in three- (or higher) way arrays, which is the case for most environmental data sets. In the future, applying N-way methods will become an increasingly popular way to uncover hidden information in complex data sets. The reason for this is that classical two-way approaches such as principal component analysis are not as good at revealing the complex relationships present in data sets. This study describes in detail the application of a chemometric N-way approach, namely the Tucker method, in order to evaluate the level of pollution in soil from a contaminated site. The analyzed soil data set was five-way in nature. The samples were collected at different depths (way 1) from two locations (way 2) and the levels of thirteen metals (way 3) were analyzed using a four-step-sequential extraction procedure (way 4), allowing detailed information to be obtained about the bioavailability and activity of the different binding forms of the metals. Furthermore, the measurements were performed under two conditions (way 5), inert and non-inert. The preferred Tucker model of definite complexity showed that there was no significant difference in measurements analyzed under inert or non-inert conditions. It also allowed two depth horizons, characterized by different accumulation pathways, to be distinguished, and it allowed the relationships between chemical elements and their biological activities and mobilities in the soil to be described in detail.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3a,b
Fig. 4a,b
Fig. 5
Fig. 6

Similar content being viewed by others

References

  1. Barbieri P, Andersson CA, Massart DL, Predonzani S, Adami G, Reisenhofer E (1999) Modeling bio-geochemical interactions in the surface waters of the Gulf of Trieste by three-way principal component analysis (PCA). Anal Chim Acta 398:227–235

    Article  CAS  Google Scholar 

  2. Barbieri P, Adami G, Piselli P, Gemiti F, Reisenhofer E (2002) A three-way principal factor analysis for assessing the time variability of freshwaters related to a municipal water supply. Chemomet Intell Lab Syst 62:89–100

    Article  CAS  Google Scholar 

  3. Stanimirova I, Simeonov V (2005) Modeling of environmental four-way data from air quality control. Chemometr Intell Lab Syst 77:115–121

    CAS  Google Scholar 

  4. Zehl K, Einax JW (2005) Influence of atmospheric oxygen on heavy metals mobility in sediment and soil. J Soils Sediments 5:164–170

    Article  CAS  Google Scholar 

  5. Smilde A, Bro R, Geladi P (2004) Multi-way analysis. Applications in the chemical sciences. Wiley, Chichester

  6. Günther R (1990) Erhebung zur Cd—Belastung durch die Leuchtstoffe und Feinchemikalien GmbH im Raum Bad Liebenstein. Abschlussarbeit zum postgradualen Studium im Umweltschutz, Bad Salzungen, pp 1–5

  7. Zehl K (2005) Schwermetalle in Sedimenten und Bödenunter besonderer Berücksichtigung der Mobilität und deren Beeinflussung durch Sauerstoff. Doctoral thesis, Jena, Germany

  8. Federal Soil Protection and Contaminated Sites Ordinance (BBodSchV) (1998) BGB1 I:502

  9. Ure AM, Quevauviller P, Muntau H, Griepink B (1993) Speciation of heavy metals in soils and sediments. An account of the improvement and harmonization of extraction techniques undertaken under the auspices of the BCR of the Commission of the European Communities. Int J Environ Anal Chem 51:135–151

    Google Scholar 

  10. Geladi P (1989) Analysis of multi-way (multi-mode) data. Chemometr Intell Lab Syst 7:11–30

    Article  CAS  Google Scholar 

  11. Bro R (1998) Multi-way analysis in food industry. Models, algorithms and applications. Doctoral thesis, Copenhagen, Denmark

  12. Henrion R (1999) N-way principal component analysis. Theory, algorithms and applications. Chemomet Intell Lab Syst 25:295–309

    Google Scholar 

  13. Andersson CA, Bro R (1998) Improving the speed of multi-way algorithms. Part I. Tucker3. Chemometr Intell Lab Syst 42:93–103

    Article  CAS  Google Scholar 

  14. Paatero P, Andersson CA (1999) Further improvements of the speed of the Tucker3 three-way algorithm. Chemometr Intell Lab Syst 47:17–20

    Article  CAS  Google Scholar 

  15. Helsel DR (2005) Nondetects and data analysis. Statistics for censored environmental data. Wiley, Hoboken, NJ

  16. Dempster AP, Laird NM, Rubin DB (1977) Maximum likelihood for incomplete data via the EM algorithm. J Roy Stat Soc B 39:1–38

    Google Scholar 

  17. Helsel DR, Hirsch RM (1992) Statistical methods in water resources. Elsevier, Amsterdam

  18. Walczak B, Massart DL (2001) Dealing with missing data: Part I. Chemometr Intell Lab Syst 58:15–27

    Article  CAS  Google Scholar 

  19. Bro R, Smilde A (2003) Centering and scaling in component analysis. J Chemometrics 17:16–33

    Article  CAS  Google Scholar 

  20. Vandeginste BMG, Massart DL, Buydens LMC, de Jong S, Lewi PJ, Smeyers-Verbeke J (1998) Handbook of chemometrics and qualimetrics: Part B. Elsevier, Amsterdam

  21. Ure AM (1996) Single extraction schemes for soil analysis and related applications. Sci Total Environ 178:3–10

    Article  CAS  Google Scholar 

  22. Einax JW, Nischwitz V (2001) Inert sampling and sample preparation—the influence of oxygen on heavy metal mobility in river sediments. Fresenius J Anal Chem 371:643–651

    Google Scholar 

  23. Paschke M, Valdecantos A, Redente E (2005) Manganese toxicity threshold for restoration grass species. Environ Pollut 135:313–322

    Article  CAS  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Y. Vander Heyden.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Stanimirova, I., Zehl, K., Massart, D.L. et al. Chemometric analysis of soil pollution data using the Tucker N-way method. Anal Bioanal Chem 385, 771–779 (2006). https://doi.org/10.1007/s00216-006-0445-y

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00216-006-0445-y

Keywords

Navigation