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Elemental fingerprint analysis of barley (Hordeum vulgare) using inductively coupled plasma mass spectrometry, isotope-ratio mass spectrometry, and multivariate statistics

Overview of attention for article published in Analytical & Bioanalytical Chemistry, October 2003
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Title
Elemental fingerprint analysis of barley (Hordeum vulgare) using inductively coupled plasma mass spectrometry, isotope-ratio mass spectrometry, and multivariate statistics
Published in
Analytical & Bioanalytical Chemistry, October 2003
DOI 10.1007/s00216-003-2219-0
Pubmed ID
Authors

Søren Husted, Birgitte F. Mikkelsen, Jacob Jensen, Niels Erik Nielsen

Abstract

Inductively coupled plasma mass spectrometry (ICP-MS) and isotope-ratio mass spectrometry (IR-MS) have been used to examine the multi-elemental composition and (15)N/(14)N and (13)C/(12)C isotope ratios of three spring barley (Hordeum vulgare) genotypes (Orthega, Barke, and Bartok) grown in three typical Danish agricultural soils (North Jutland, West Jutland, and East Zealand) differing in soil fertility. The aim of the study was to examine whether it was possible to generate a unique elemental fingerprint of individual barley genotypes irrespective of the elemental imprint plants had received from soils differing in fertility and agricultural practice. Multivariate statistics were used to analyze the elemental fingerprints of the barley genotypes at different times during a full growing season from early tillering to full maturity of the barley grains. Initially, 36 elements were analyzed in the plant samples but this number was subsequently reduced to 15 elements: B, Ba, C, Ca, Cu, Fe, K, Mg, Mn, N, Na, P, S, Sr, and Zn. These elements exceeded the limit of detection ( LOD) for all genotypes, soil types, and plant growth stages and for these elements the accuracy was better than 90% compared with apple leaf certified reference material (CRM). Principal component analysis (PCA) separated multi-elemental data in accordance with soil type when plants of similar physiological age were compared, whereas this separation disappeared if plants of all ages were compared simultaneously. Isotope ratios (delta(15)N) of plants also proved to be a highly accurate property for classification of samples according to soil type. In contrast, the differences in delta(13)C were too small to enable such classification. The differences in delta(15)N among soils were so pronounced that separation of samples according to the physiological age of plants became redundant. However, delta(15)N and the multi-elemental analysis revealed no differences between the three barley genotypes, indicating that the influence of soil chemistry and possibly also climate and agricultural practice was too large to allow an unique elemental fingerprint for the genotypes. This finding was substantiated by analyzing the multi-elemental composition of grain from two additional genotypes (Otira and Barthos) grown at the north and east locations, respectively. PCA showed not only that the elemental fingerprints of these two genotypes were similar to those of the others, but also that the soil in which the plant had been growing could be accurately predicted on the basis of the PCA scores from the genotypes Orthega, Barke, and Bartok. Similar conclusions could be drawn using delta(15)N data.

Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 41 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
India 1 2%
United States 1 2%
Canada 1 2%
Unknown 38 93%

Demographic breakdown

Readers by professional status Count As %
Researcher 12 29%
Student > Master 5 12%
Student > Ph. D. Student 4 10%
Student > Doctoral Student 2 5%
Student > Bachelor 2 5%
Other 7 17%
Unknown 9 22%
Readers by discipline Count As %
Agricultural and Biological Sciences 14 34%
Environmental Science 4 10%
Earth and Planetary Sciences 3 7%
Biochemistry, Genetics and Molecular Biology 2 5%
Chemistry 2 5%
Other 4 10%
Unknown 12 29%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 3. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 26 November 2023.
All research outputs
#8,535,472
of 25,374,647 outputs
Outputs from Analytical & Bioanalytical Chemistry
#2,202
of 9,619 outputs
Outputs of similar age
#19,892
of 56,731 outputs
Outputs of similar age from Analytical & Bioanalytical Chemistry
#10
of 32 outputs
Altmetric has tracked 25,374,647 research outputs across all sources so far. This one is in the 43rd percentile – i.e., 43% of other outputs scored the same or lower than it.
So far Altmetric has tracked 9,619 research outputs from this source. They receive a mean Attention Score of 3.1. This one has gotten more attention than average, scoring higher than 56% of its peers.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 56,731 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 12th percentile – i.e., 12% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 32 others from the same source and published within six weeks on either side of this one. This one is in the 3rd percentile – i.e., 3% of its contemporaries scored the same or lower than it.