FOI CBRN Defence and Security, Swedish Defence Research Agency, SE-90182 Umeå, Sweden.
Chemical and Biological Signature Sciences, National Security Directorate, Pacific Northwest National Laboratory, P.O Box 999, MSIN P7-50 Richland, WA, United States.
Talanta. 2018 Aug 15;186:628-635. doi: 10.1016/j.talanta.2018.03.070. Epub 2018 Mar 28.
A forensic method for the retrospective determination of preparation methods used for illicit ricin toxin production was developed. The method was based on a complex set of biomarkers, including carbohydrates, fatty acids, seed storage proteins, in combination with data on ricin and Ricinus communis agglutinin. The analyses were performed on samples prepared from four castor bean plant (R. communis) cultivars by four different sample preparation methods (PM1-PM4) ranging from simple disintegration of the castor beans to multi-step preparation methods including different protein precipitation methods. Comprehensive analytical data was collected by use of a range of analytical methods and robust orthogonal partial least squares-discriminant analysis- models (OPLS-DA) were constructed based on the calibration set. By the use of a decision tree and two OPLS-DA models, the sample preparation methods of test set samples were determined. The model statistics of the two models were good and a 100% rate of correct predictions of the test set was achieved.
建立了一种回溯性测定制备非法蓖麻毒素方法的法医方法。该方法基于一组复杂的生物标志物,包括碳水化合物、脂肪酸、种子储存蛋白,结合蓖麻毒素和蓖麻凝集素的数据。对通过四种不同的样品制备方法(PM1-PM4)制备的来自四个蓖麻品种的样品进行了分析,这些方法从简单的蓖麻粉碎到包括不同蛋白质沉淀方法的多步制备方法。通过使用一系列分析方法收集全面的分析数据,并基于校准集构建了稳健的正交偏最小二乘判别分析模型(OPLS-DA)。使用决策树和两个 OPLS-DA 模型,确定了测试集样品的样品制备方法。两个模型的模型统计数据良好,测试集的预测准确率达到 100%。