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Technical note: A linear model for predicting delta C-13(protein)

机译:技术说明:预测δC-13(蛋白质)的线性模型

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Objective: Development of a model for the prediction of C-13(protein) from C-13(collagen) and C-13(ap-co). Model-generated values could, in turn, serve as consumer inputs for multisource mixture modeling of paleodiet. Methods: Linear regression analysis of previously published controlled diet data facilitated the development of a mathematical model for predicting C-13(protein) (and an experimentally generated error term) from isotopic data routinely generated during the analysis of osseous remains (C-13(co) and C-13(ap-co)). Results: Regression analysis resulted in a two-term linear model (C-13(protein) (%)=(0.78 x C-13(co))-(0.58x C-13(ap-co))-4.7), possessing a high R-value of 0.93 (r(2)=0.86, P < 0.01), and experimentally generated error terms of +/- 1.9% for any predicted individual value of C-13(protein). This model was tested using isotopic data from Formative Period individuals from northern Chile's Atacama Desert. Conclusions: The model presented here appears to hold significant potential for the prediction of the carbon isotope signature of dietary protein using only such data as is routinely generated in the course of stable isotope analysis of human osseous remains. These predicted values are ideal for use in multisource mixture modeling of dietary protein source contribution. Am J Phys Anthropol 157:694-703, 2015. (c) 2015 Wiley Periodicals, Inc.
机译:目的:开发一种从C-13(胶原蛋白)和C-13(ap-co)中预测C-13(蛋白质)的模型。反过来,模型生成的值可以用作消费者对古生物的多源混合建模的输入。方法:对先前发表的控制饮食数据进行线性回归分析,有助于开发一种数学模型,用于根据骨质残留分析过程中常规生成的同位素数据预测C-13(蛋白质)(以及实验产生的误差项)。 co)和C-13(ap-co))。结果:回归分析得出了一个两阶段线性模型(C-13(蛋白质)(%)=(0.78 x C-13(co))-(0.58x C-13(ap-co))-4.7),具有0.93的高R值(r(2)= 0.86,P <0.01),并且对于任何预测的C-13(蛋白质)单个值,实验生成的误差项为+/- 1.9%。使用来自智利北部阿塔卡马沙漠形成期个体的同位素数据对该模型进行了测试。结论:这里介绍的模型似乎具有巨大的潜力,可以仅使用在对人体骨残留物进行稳定同位素分析过程中常规生成的数据来预测膳食蛋白质的碳同位素特征。这些预测值非常适合用于饮食蛋白源贡献的多源混合建模。 Am J Phys Anthropol 157:694-703,2015年。(c)2015 Wiley Periodicals,Inc.

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