Selectivity is one of the main challenges of sensors, particularly those based on chemical interactions. Multivariate analytical models can determine the concentration of analytes even in the presence of other potential interferences. In this work, we have determined the presence of mercury ions in aqueous solutions in the ppm range (0-2 mg L^-1) using a ruthenium bis-thiocyanate complex as a chemical probe. Moreover, we have analyzed the mercury-containing solutions with the co-existence of higher concentrations (19.5 mg L^-1) of other potential competitors such as Cd²⁺, Pb²⁺, Cu²⁺ and Zn²⁺ ions. Our experimental model is based on partial least squares (PLS) method and other techniques as genetic algorithm and statistical feature selection (SFS) that have been used to refine, beforehand, the analytical data. In summary, we have demonstrated that the root mean square error of prediction without pre-treatment and with statistical feature selection can be reduced from 10.22% to 6.27%.