About this Journal Submit a Manuscript Table of Contents
Journal of Biomedicine and Biotechnology
Volume 2012 (2012), Article ID 450967, 8 pages
http://dx.doi.org/10.1155/2012/450967
Research Article

Bayesian Integration of Isotope Ratio for Geographic Sourcing of Castor Beans

1Computational Biology and Bioinformatics, Pacific Northwest National Laboratory, Richland, WA 99352, USA
2Biodefense, Pacific Northwest National Laboratory, Richland, WA 99352, USA
3Nuclear Material Analysis, Pacific Northwest National Laboratory, Richland, WA 99352, USA
4Department of Geology and Geophysics, The University of Utah, Salt Lake City, UT 84112, USA
5Department of Ecosystem Science and Management, Texas A&M University, College Station, TX 77843, USA
6Marine Sciences Laboratory, Pacific Northwest National Laboratory, Sequim, WA 98382, USA

Received 29 February 2012; Accepted 13 May 2012

Academic Editor: Carlos Ramos

Copyright © 2012 Bobbie-Jo Webb-Robertson et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

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.