چکیده (انگلیسی):

One of the main problems of quantitative analytical chemistry is to
estimate the concentration of one or more species from the values of certain
physicochemical properties of the system of interest. For this it is necessary
to construct a calibration model, i.e., to determine the relationship between
measured properties and concentrations. The multivariate calibration is one of
the most successful combinations of statistical methods to chemical data, both
in analytical chemistry and in theoretical chemistry. Among used methods
can cite Artificial Neural Networks (ANN), the Nonlinear Partial Least
Squares (N-PLS), Principal Components Regression (PCR) and Multiple
Linear Regression (MLR). In addition of multivariate calibration methods
algorithms of samples selection are used. These algorithms choose a subset of
samples to be used in training set covering adequately the space of the
samples. In other hand, a large spectrum of a sample is typically measured by
modern scanning instruments generating hundreds of variables. Search
algorithms have been used to identify variables which contribute useful
information about the dependent variable in the model. This paper proposes a
Genetic Algorithm based on Double Chromosome (GADC) to do these tasks
simultaneously, the sample and variable selection. The obtained results were
compared with the well-known algorithms for samples and variable selection
Kennard-Stone, Partial Least Square and Successive Projection Algorithm. We
showed that the proposed algorithm can obtain better calibrations models in a
case study involving the determination of content protein in wheat samples.