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Title: Bayesian Integration of Isotope Ratio for Geographic Sourcing of Castor Beans

Recent years have seen an increase in the forensic interest associated with the poison ricin, which is extracted from the seeds of the Ricinus communis plant. Both light element (C, N, O, and H) and strontium (Sr) isotope ratios have previously been used to associate organic material with geographic regions of origin. We present a Bayesian integration methodology that can more accurately predict the region of origin for a castor bean than individual models developed independently for light element stable isotopes or Sr isotope ratios. Our results demonstrate a clear improvement in the ability to correctly classify regions based on the integrated model with a class accuracy of 60.9 ± 2.1 % versus 55.9 ± 2.1 % and 40.2 ± 1.8 % for the light element and strontium (Sr) isotope ratios, respectively. In addition, we show graphically the strengths and weaknesses of each dataset in respect to class prediction and how the integration of these datasets strengthens the overall model.
 [1] ;  [2] ;  [3] ;  [4] ;  [5] ;  [6] ;  [3]
  1. Computational Biology and Bioinformatics, Pacific Northwest National Laboratory, Richland, WA 99352, USA
  2. Biodefense, Pacific Northwest National Laboratory, Richland, WA 99352, USA
  3. Nuclear Material Analysis, Pacific Northwest National Laboratory, Richland, WA 99352, USA
  4. Department of Geology and Geophysics, The University of Utah, Salt Lake City, UT 84112, USA
  5. Department of Ecosystem Science and Management, Texas A&M University, College Station, TX 77843, USA
  6. Marine Sciences Laboratory, Pacific Northwest National Laboratory, Sequim, WA 98382, USA
Publication Date:
Grant/Contract Number:
Published Article
Journal Name:
Journal of Biomedicine and Biotechnology
Additional Journal Information:
Journal Volume: 2012; Related Information: CHORUS Timestamp: 2017-06-20 17:21:36; Journal ID: ISSN 1110-7243
Hindawi Publishing Corporation
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