Geochemical Signatures as a Tool for Vermiculite Provenance Determination

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Thirty-eight samples of known origin (China, Libby MT, South Africa, South Carolina) and 6 vermiculite product samples of unknown origin were analyzed for major and trace elements, including rare earth elements to determine the feasibility of distinguishing the provenance of the samples based upon a geochemical signature. Probability plots suggest that two of the four groups (Libby, South Carolina) were comprised of two subgroups. Results of hierarchical cluster analysis are highly sensitive to the linkage method chosen. Ward’s method is the most useful for this data and suggests that there are five groups within the data set (South African samples, ... continued below

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Wright, Karen E. & Palmer, Carl D. September 1, 2008.

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Thirty-eight samples of known origin (China, Libby MT, South Africa, South Carolina) and 6 vermiculite product samples of unknown origin were analyzed for major and trace elements, including rare earth elements to determine the feasibility of distinguishing the provenance of the samples based upon a geochemical signature. Probability plots suggest that two of the four groups (Libby, South Carolina) were comprised of two subgroups. Results of hierarchical cluster analysis are highly sensitive to the linkage method chosen. Ward’s method is the most useful for this data and suggests that there are five groups within the data set (South African samples, two subsets of the Libby samples, a subset of the South Carolina samples, and a second subset of the South Carolina samples combined with the China samples). Similar results were obtained using k-cluster analysis. Neither clustering method was able to distinguish samples from China from the South Carolina samples. Discriminant analysis was used on a four-category model comprised of the original four groups and on a six-category model comprised of the five categories identified from the cluster analysis but with the China samples grouped into a sixth category. The discriminant/classification model was able to distinguish all of the groups including the China samples from one another for both the four- and six-category models with 100% of the samples properly classified. The 6 unknown product samples were classified with a probability of consistency of 99%. Both discriminant models were also run with a subset of our analyte set to be consistent with the smaller Gunter et al., (2005) analyte set. Twenty vermiculite samples (nine of known origin and eleven of unknown origin) from their study were classified based on our discriminant models with the reduced set of analytes. Of the twenty samples, Gunter et al. (2005) was able to classify 16 with cluster analysis while our 4-category discriminant analysis model allowed us to classify all twenty. Of the 16 samples Gunter et al. (2005) classified using cluster analysis, all but one sample was assigned the same classification by our 4-category model. Of the nine samples with known origin, all were correctly classified. Similar results were obtained for the six-category model. Comparison of the plots of the canonical roots, the Wilks’ L, and the square Mahalanobis distances suggest the full analyte set provides better discrimination of the groups than the reduced analyte set. The six-category model is more consistent with the results of the probability plots and the cluster analysis. Discriminant analysis of geochemical data from vermiculite ore is a powerful technique for determining the ore’s provenance.

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  • Report No.: INL/EXT-08-14828
  • Grant Number: DE-AC07-99ID-13727
  • DOI: 10.2172/941739 | External Link
  • Office of Scientific & Technical Information Report Number: 941739
  • Archival Resource Key: ark:/67531/metadc900785

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  • September 1, 2008

Added to The UNT Digital Library

  • Sept. 27, 2016, 1:39 a.m.

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  • Nov. 7, 2017, 6:11 p.m.

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Wright, Karen E. & Palmer, Carl D. Geochemical Signatures as a Tool for Vermiculite Provenance Determination, report, September 1, 2008; [Idaho Falls, Idaho]. (digital.library.unt.edu/ark:/67531/metadc900785/: accessed December 13, 2017), University of North Texas Libraries, Digital Library, digital.library.unt.edu; crediting UNT Libraries Government Documents Department.