We introduce a new variable selection approach, which converges much faster to the optimal set of variables for a given application. The new procedure runs in two steps. First, a coarse and very fast variable selection procedure is applied: a figure of merit is defined and computed for every variable, a threshold value set and only the variables whose figure of merit is higher than the threshold are retained for further selection. Then, a fine-tuning selection based either on deterministic or stochastic methods is conducted on the variable subset that resulted from the first step. The method is demonstrated using a database consisting of vapors of acetone, ammonia and o-xylene and their binary mixtures (120 variables/measurement). Vapors can be simultaneously identified and quantified with a 92.7% success rate and the time needed for variable selection is reduced at least by a factor of 4.