Fingerprinting technique is a widely used tool to assess the sources of sediments and particle bound chemicals within a watershed, and the results obtained from unmixing models are becoming valuable data to support soil and water resources monitoring and conservation. Nowadays, numerous studies have used fingerprinting techniques to examine specific catchment management problems.

Despite its shortcomings and the lack of standardization, the technique continues on an upward trend globally. This paper takes a new look at the utility of the mostly used tracer selection methods and their influence when using fingerprinting models.

Furthermore, the increase in the analysis capabilities and the use of more tracers than n-1 tracers (where n is the number of sources) for unmixing leads to the possibility of mathematical inconsistency and the existence of multiple solutions in the analysis of a particular mixture, which is a possible source of errors that remains unexplored nowadays.

Within the framework of these criteria, we have i) inspected if both types of models, Frequentist and Bayesian, are sensitive to tracers with erroneous information; ii) examined the most commonly used tracer selection methods; iii) tested the consistency and the existence of multiple solutions in over-determined systems and iv) devised a Consistent Tracer Selection (CTS) method to extract the solutions present in the dataset.

The strength of this novel study lies in the valuable and useful tracer selection method that has been presented. Frequentist model such as FingerPro and a Bayesian model, MixSIAR, are implemented to test the method. Both models agreed on their solutions when selecting the tracers based on the new method, while both disagreed when selecting the tracers following previous methods. The new CTS method's ability to extract the multiple discriminant and consistent solutions inside fingerprinting datasets has no precedent in the literature.

指纹技术是一种广泛使用的评估流域内沉积物和颗粒结合化学物质来源的工具，从分解模型获得的结果正成为支持土壤和水资源监测和保护的有价值的数据。如今，许多研究已使用指纹技术来检查特定的流域管理问题。

尽管其缺点和缺乏标准化，但该技术在全球范围内仍呈上升趋势。本文重新介绍了最常用的示踪剂选择方法的实用性及其在使用指纹模型时的影响。

此外，分析能力的提高以及与n-1个示踪剂（其中n是来源的数量）相比使用更多的示踪剂进行解混会导致数学上不一致的可能性，并且在特定混合物的分析中存在多个解决方案，这是当今仍未发现的错误的可能来源。

在这些标准的框架内，我们（i）检查了两种类型的模型（频繁模型和贝叶斯模型）是否对带有错误信息的示踪剂敏感；ii）研究了最常用的示踪剂选择方法；iii）测试了超定系统中多个解决方案的一致性和存在性，并且iv）设计了一致性示踪剂选择（CTS）方法来提取数据集中存在的解决方案。

这项新颖的研究的优势在于已提出的有价值和有用的示踪剂选择方法。实现了常用的模型（例如FingerPro）和贝叶斯模型MixMIAR来测试该方法。两种模型在基于新方法选择示踪剂时都同意他们的解决方案，而在按照以前的方法选择示踪剂时都不一致。新的CTS方法能够提取指纹数据集中的多个判别和一致解，这在文献中没有先例。