Odour nuisance can be caused by industrial emissions. Sensory analyses are efficient approaches when assessing these odours. However, two obstacles may interfere with sensory analyses and make odour sources identification harder: the subjectivity of the human panel and the poorly understood effect of odour mixtures on the quality of the final odour, when industrial emissions get mixed. To answer that question, an approach is proposed in this article combining the experimental mixture design with the Langage des Nez ® , a method that uses chemical referents as odour descriptors reducing the subjectivity of the panel. Three odorous compounds were studied: propyl mercaptan, α-pinene and furfuryl mercaptan. They were mixed at different odour activity values. For each mixture, a sensory analysis was made to describe the odour with the Langage des Nez ®. The variation of the odour profile with the composition was modelled. The obtained models were validated and represented in a 3D space enabling the visualisation of the evolution of the models. This approach is considered a cornerstone in better understanding the effect of odours mixtures thus removing this obstacle when assessing odour nuisance with the objective of identifying the odour sources using sensory analyses.