Fernandes Ricardo, Millard Andrew R, Brabec Marek, Nadeau Marie-Josée, Grootes Pieter
Leibniz Laboratory for Radiometric Dating and Isotope Research, Christian-Albrechts-Universität zu Kiel, Kiel, Germany ; Graduate School "Human Development in Landscapes", Christian-Albrechts-Universität zu Kiel, Kiel, Germany.
Department of Archaeology, Durham University, Durham, United Kingdom.
PLoS One. 2014 Feb 13;9(2):e87436. doi: 10.1371/journal.pone.0087436. eCollection 2014.
Human and animal diet reconstruction studies that rely on tissue chemical signatures aim at providing estimates on the relative intake of potential food groups. However, several sources of uncertainty need to be considered when handling data. Bayesian mixing models provide a natural platform to handle diverse sources of uncertainty while allowing the user to contribute with prior expert information. The Bayesian mixing model FRUITS (Food Reconstruction Using Isotopic Transferred Signals) was developed for use in diet reconstruction studies. FRUITS incorporates the capability to account for dietary routing, that is, the contribution of different food fractions (e.g. macronutrients) towards a dietary proxy signal measured in the consumer. FRUITS also provides relatively straightforward means for the introduction of prior information on the relative dietary contributions of food groups or food fractions. This type of prior may originate, for instance, from physiological or metabolic studies. FRUITS performance was tested using simulated data and data from a published controlled animal feeding experiment. The feeding experiment data was selected to exemplify the application of the novel capabilities incorporated into FRUITS but also to illustrate some of the aspects that need to be considered when handling data within diet reconstruction studies. FRUITS accurately predicted dietary intakes, and more precise estimates were obtained for dietary scenarios in which expert prior information was included. FRUITS represents a useful tool to achieve accurate and precise food intake estimates in diet reconstruction studies within different scientific fields (e.g. ecology, forensics, archaeology, and dietary physiology).
依靠组织化学特征的人类和动物饮食重建研究旨在提供潜在食物组相对摄入量的估计值。然而,在处理数据时需要考虑几个不确定性来源。贝叶斯混合模型提供了一个自然的平台来处理各种不确定性来源,同时允许用户提供先验专家信息。贝叶斯混合模型FRUITS(利用同位素转移信号进行食物重建)是为饮食重建研究而开发的。FRUITS具有考虑饮食路径的能力,即不同食物组分(如宏量营养素)对消费者体内测量的饮食替代信号的贡献。FRUITS还提供了相对直接的方法来引入关于食物组或食物组分相对饮食贡献的先验信息。这种先验信息可能例如源自生理学或代谢研究。使用模拟数据和来自已发表的对照动物喂养实验的数据对FRUITS的性能进行了测试。选择喂养实验数据是为了举例说明FRUITS所包含的新功能的应用,同时也说明在饮食重建研究中处理数据时需要考虑的一些方面。FRUITS准确地预测了饮食摄入量,并且对于包含专家先验信息的饮食情况获得了更精确的估计。FRUITS是在不同科学领域(如生态学、法医学、考古学和饮食生理学)的饮食重建研究中实现准确和精确的食物摄入量估计的有用工具。