Pollution greatly impacts ecosystems health and associated ecological functions. Persistent Organic Pollutants (POPs) are among the most studied contaminants due to their persistence, bioaccumulation, and toxicity potential. Biomagnification is often described using the estimation of a Trophic Magnification Factor (TMF). This estimate is based on the relationship between contamination levels of the species and their trophic level. However, while the estimation can be significantly biased in relation to multiple sources of uncertainty (e.g. species physiology, measurement errors, food web complexity), usual TMF estimation methods typically do not allow accounting for these potential biases. More accurate and reliable assessment tool of TMFs and their associated uncertainty are therefore needed in order to appropriately guide chemical pollution management. The present work proposes a relevant and innovative TMF estimation method accounting for its many variability sources. The ESCROC model (EStimating Contaminants tRansfers Over Complex food webs), which is implemented in a Bayesian framework, allows for a more reliable and rigorous assessment of contaminants trophic magnification, in addition to accurate estimations of isotopes trophic enrichment factors and their associated uncertainties in food webs. Similar to classical mixing models used in food web investigations, ECSROC computes diet composition matrices using isotopic composition data while accounting for contamination data, leading to more robust food web descriptions. As a demonstration of the practical application of the model, ESCROC was implemented to revisit the trophic biomagnification of 5 polyfluoroalkyl substances (PFAS) in a complex estuarine food web (the Gironde, SW France). In addition to the TMF estimate and 95% confidence intervals, the model provided biomagnification probabilities associated to the investigated contaminants.