不同城市居民头发中氧、氢稳定同位素检验和特征初探

Examination and Analysis on Oxygen and Hydrogen Stable Isotope Ratios of Resident Hair

目的人体组织的稳定同位素组成与其生长期间的个体饮食情况、所处环境及代谢状况有关。人头发一经长出便不再与身体进行物质交换,化学性质稳定,易于采集,是研究人体组织稳定同位素组成的良好对象。构成人体的氧、氢元素主要来自于所摄入的水和食物,其中氧、氢稳定同位素组成会通过角蛋白的形式被记录于头发当中。不同地区居民头发中氧、氢稳定同位素组成差异可被用于推断人的饮食情况、生活地域和活动轨迹信息,在法庭科学等研究领域具有重要意义。方法本研究利用元素分析仪-稳定同位素比质谱仪(EA-IRMS)对国内不同地区常住居民头发样本进行氧、氢稳定同位素比值检测和分析。结果部分城市间居民头发δ^(18)O和δ^(2)H存在显著性差异,整体δ^(18)O和δ^(2)H存在显著正相关性。对所得稳定同位素数据进行判别分析推断头发的地域来源,其交互验证整体判别准确率为63.9%,结合碳、氮稳定同位素数据后,其判别准确率大幅提升,交互验证的整体判别准确率达到76.0%。随着判别分析中使用的稳定同位素种类的增加,判别函数模型的判别能力明显增强。结论利用4种元素稳定同位素数据建立的多层感知器神经网络模型的整体判别准确率为82.8%,径向基函数神经网络模型整体判别准确率为78.8%,3种溯源推断数学模型中,多层感知器神经网络模型的判别准确率最高。

Objective The stable isotopic composition of human tissue is related to the individual’s diet,environment and metabolic status.Once grown,human hair is no longer exchanged with the body and is chemically stable and easy to collect,making it a good subject for studying the stable isotope composition of human tissue.The oxygen and hydrogen elements that make up the human body are mainly derived from the water and food,of which the oxygen and hydrogen stable isotope composition is recorded in hair keratin.Differences in the stable isotope composition of oxygen and hydrogen in the human hair of different regions can be used to infer information about people’s diets,living areas and activity trajectories,which is of great importance in forensic science and other research fields.Methods In this study,oxygen and hydrogen stable isotope ratios of hair samples from permanent residents in different regions of China were detected and analyzed using an elemental analyzer-isotope ratio mass spectrometer(EA-IRMS).Results The results showed that there were significant differences inδ^(18)O andδ^(2)H between some cities,and a significant positive correlation between overallδ^(18)O andδ^(2)H.The overall discrimination accuracy of the stable isotope data was 63.9%for the crossvalidation,and increased significantly when combined with the carbon and nitrogen stable isotope data,reaching an overall discrimination accuracy of 76.0%for the cross-validation.As the type of stable isotopes used in the discriminant analysis increased,the model’s discriminant ability was significantly enhanced.Conclusion The overall discriminative accuracy of the multi-layer perceptron neural network model built by using the stable isotope data of the 4 elements was 82.8%,and the overall discriminative accuracy of the radial basis function neural network model was 78.8%.The multilayer perceptron neural network model had the highest discriminative accuracy among the three traceability mathematical models.