Bayesian Integration of Isotope Ratio for Geographic Sourcing of Castor Beans

[摘要] Recent years have seen an increase in the forensic interest associated with the poison ricin, which is extracted from the seeds of theRicinus communisplant. 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 of60.9±2.1% versus55.9±2.1% and40.2±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.

[学科分类] 基础医学