Bayesian Integration of Isotope Ratios 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 6 0 . 9 {+-} 2 . 1 % versus 5 5 . 9 {+-} 2 . 1 % and 4 0 . 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.
- Research Organization:
- Pacific Northwest National Lab. (PNNL), Richland, WA (United States)
- Sponsoring Organization:
- USDOE
- DOE Contract Number:
- AC05-76RL01830
- OSTI ID:
- 1050800
- Report Number(s):
- PNNL-SA-83058; TRN: US201218%%1569
- Journal Information:
- Journal of Biomedicine and Biotechnology, Vol. 2012
- Country of Publication:
- United States
- Language:
- English
Similar Records
Forensic Applications of Light-Element Stable Isotope Ratios of Ricinus communis Seeds and Ricin Preparations
A Ricin Forensic Profiling Approach Based on a Complex Set of Biomarkers