Publication Details

Category Text Publication
Reference Category Journals
DOI 10.1016/j.microc.2013.12.006
Title (Primary) Element pattern recognition and classification in sunflowers (Helianthus annuus) grown on contaminated and non-contaminated soil
Author Kötschau, A.; Büchel, G.; Einax, J.W.; Meißner, R.; von Tümpling, W. ORCID logo ; Merten, D.
Source Titel Microchemical Journal
Year 2014
Department FLOEK; BOPHY
Volume 114
Page From 164
Page To 174
Language englisch
Keywords Sunflower; Cluster analysis; Linear discriminant analysis; Display methods; Element pattern; Bioavailability
UFZ wide themes RU1

This study aims on identifying growth site and plant part specific element patterns in sunflower (Helianthus annuus).

Sunflowers (H. annuus) were planted in small-scaled plots under field conditions on a metal-contaminated and a non-contaminated site over a vegetation period of 170 days. Nitric acid soluble contents of Ca, Cd, Co, Cr, Cu, Fe, K, Mg, Mn, Ni, P, Pb, S, Th, U, and Zn were determined in roots, stems, leaves, and heads of sunflowers harvested in regular time intervals during the vegetation period. Bioavailable and total contents of the mentioned elements were determined in the corresponding soil taken at the day of sowing and after the last harvest.

At first, hierarchical agglomerative cluster analysis was used to investigate the similarities and differences between elements in sunflower parts based on 16 elements and 78 samples. Thereby two clusters were formed, containing separated from each other the sunflower samples from the contaminated and the non-contaminated site. Therein several smaller sub-clusters were found, containing the single plant parts: roots, stems, leaves, and heads.

Forward stepwise linear discriminant analysis was used to verify the clusters of plant parts and to identify the elements which show the highest discriminating power in the data with respect to the sunflower parts and the plots on which the sunflowers were grown. A linear discriminant model based on Mn, Ca, Fe, Ni, U, and Zn out of the 16 measured elements allowed distinguishing between the tissue types from the contaminated and non-contaminated site (correct classification of 91.7%). By plotting the autoscaled data of these most discriminating elements the plant part and site specific element patterns could be displayed. Furthermore an explanation is given how the bioavailability of elements in soil and plant physiology influence these element patterns.

Persistent UFZ Identifier
Kötschau, A., Büchel, G., Einax, J.W., Meißner, R., von Tümpling, W., Merten, D. (2014):
Element pattern recognition and classification in sunflowers (Helianthus annuus) grown on contaminated and non-contaminated soil
Microchem J. 114 , 164 - 174 10.1016/j.microc.2013.12.006