Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)This paper proposes a new constrained method, based on non-negative matrix factorization, for blindly separating linear-quadratic (LQ) mixtures of mutually uncorrelated source signals when the sources and mixing parameters are all non-negative. The uncorrelatedness of the sources is used as a regularization term in the cost function. The main advantage of exploiting uncorrelatedness in this manner is that the inversion of the mixing model, which is a difficult task in the case of determined LQ mixtures, is not required, contrary to the classical LQ methods based on independent component analysis. Experimental results using artificial data and real-world chemical data confirm the effectiveness of our method.National Council for ScientificTechnological Development (CNPq, Brazil)Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)26th IEEE International Workshop on Machine Learning for Signal Processing (MLSP)SEP 13-16, 2016Salerno, ITALY