Sediment source fingerprinting can be an effective method for identifying sediment sources in wildfire-impacted areas, but the effects of tracer and model selection on robustness remain poorly understood. In this study, soil samples were collected from three potential sources, and artificial mixtures with known source proportions were created. Three types of tracers were tested for their sensitivity to wildfire. Ten composite fingerprints, generated through the traditional three-step procedure (TSP) as well as consensus ranking and the conservativeness index (CM) were used to assess the accuracy of two un-mixing models (FingerPro and MixSIAR). The results indicated that wildfire had substantial effects on most tracer properties. Among the ten composite fingerprints, the CM selection method performed best. While the TSP method could achieve a near-global optimum in some cases, it was the least stable among the ten tracer sets. Compared to FingerPro, MixSIAR delivered higher accuracy and precision for our case study.