Palaeodietary reconstruction using stable isotope analysis is becoming increasingly common, as is the practice of using mixing models to quantify ancient dietary compositions. However, many archaeologists may be unaware of the complexities and pitfalls of stable isotope mixing models (SIMMs). This study serves to provide an overview of the basic principles of SIMMs, evaluates the performances of several of the most commonly used SIMM software packages, and offers some field-specific guidelines for the application of SIMMs in archaeological contexts. We present a series of simulated and published archaeological data to demonstrate and evaluate the different types of SIMMs. We compared the outputs of linear mixing models, simple probabilistic models (IsoSource), and conditional probabilistic models (FRUITS and MixSIAR). Our results show that each mixing model has its pros and cons, and archaeologists should select the best model based on a number of factors, including familiarity with coding languages, sample characteristics (i.e. sample size and normality) of the consumer groups, and research questions.

使用稳定同位素分析进行​​古饮食重建正变得越来越普遍，使用混合模型来量化古代饮食成分的做法也越来越普遍。然而，许多考古学家可能没有意识到稳定同位素混合模型 (SIMM) 的复杂性和陷阱。本研究概述了 SIMM 的基本原理，评估了几种最常用的 SIMM 软件包的性能，并为 SIMM 在考古环境中的应用提供了一些特定领域的指南。我们提供了一系列模拟和发布的考古数据来展示和评估不同类型的 SIMM。我们比较了线性混合模型、简单概率模型 (IsoSource) 和条件概率模型（FRUITS 和 MixSIAR）的输出。即样本大小和正态性）消费者群体，以及研究问题。