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Top1. Introduction
The chemmometrics is a branch of analytical chemistry that uses knowledge mathematical, statistical, and logic to develop methods to chemical data analysis (Brown, Blank, Sum, & Weyer, 1994; Yusoff, Venkat, Yusof, & Abdullah, 2012). The main goal this area is the concentration determination of analyte collected using instrumental methods (Beebe, Pell & Seasholtz, 1998). The concentration value is obtained indirectly from direct measurements (absortion, light emission) made by the instrument using a calibration model that relates the physical measurements with the concentration of interest analyte (Skoog, 2008).
Prediction in chemmometrics is a procedure that use a multivariate model to predict the properties of a given sample. The absorbance at a wavelength can be related to the concentration of an analyte (Martens, 1989). The multivariate calibration is related to the construction of a mathematic model to calculate a predict value 
based on measured values of a set of explanatory variables
There are popular calibration models to building multivariate regression model as Multiple Linear Regression (MLR) (Martens, 1989), Principal Component Regression (PCR) (Jolliffe, 1982) and Partial Least Square Regression (PLSR) (Beebe, et al., 1998; Martens & Naes, 1989).
Sometimes, it isn’t necessary the use of all collected data of a sample during the calibration process to analyze just some features of the sample. The selection of variables with information related to these features of interest allows creating more parsimonious and simple models, which are also easy of interpretation (Gaspar-Cunha, Mendes, Duarte, Vieira, Ribeiro, Ribeiro, & Neves, 2010). Others problems also found on calibration are the collinearity and sensitivity. The collinearity happens when two or more variables have correlated information. The sensitivity to noise prejudice the calibration efficiency and prediction of the compounds of sample, in particular MLR models (Martens & Naes, 1989; Draper, Smith, & Pownell, 1966).
A solution to the collinear variables is to obliterate them through variable selection methods (Guyon, & Elisseeff, 2003). At this process, the use of evolutionary algorithms, in particular Genetic Algorithms (GAs) are promising methods. An optimization algorithm like an evolutionary algorithm can be used to choose a strong subset of variables with little redundancy and information related to the characteristics of interest (Holland, 1992).
At this work we propose the use of the multi-objective genetic algorithms NSGA-II to the variables selection process. This problem has two conflicting objectives: minimize the residual error between concentration predicted by the MLR model and the real protein concentration of the grain, and minimize the number of selected variables. When we reduce the number of selected variables we also reduce the computational cost and simplify the calibration model (Coello, Lamont, & Van Veldhuisen, 2007).