The present paper deals with gas concentration monitoring based on an electronic nose. The proposed approach investigates two regression methods for gas concentration estimation: the first is the most used in gas quantification with electronic nose and known as the partial least squares (PLS) and the second, known as the support vector machine (SVM) regression, is recently used by the electronic nose community. Data used in this work are collected using an E-nose device developed in our laboratory and responding to various concentrations of pine essential oil vapours. The comparison between the two regression methods studied in this paper is related to the accuracy, the universality as well as the number of samples needed for learning. The results are analyzed in order to select the more suitable prediction model for gas concentration estimation.