Adaptive multicomponent analysis by genetic algorithms.

Abstract

The applicability of genetic algorithms for solving multicomponent analyses is systematically examined. As a genetic algorithm (GA), the basic proposal of Goldberg is implemented in a straightforward manner to simulate multicomponent analyses in analogy to the well-established UV-vis or IR methods, especially multicomponent regression. The main focus of the study is to investigate the behavior of the genetic algorithm in order to compare it with the well-known behavior of multicomponent regression. A remarkable difference between the two methods is that the genetic algorithm method does not need any calibration procedure because of its pure searching characteristic. As important features of multicomponent systems, the degree of signal overlap (selectivity), the behavior of systems with known and unknown component numbers and qualities, and linear as well as nonlinear relationships between the analytical signal and concentration are varied within the simulations. According to multicomponent regression, recovering concentrations by a genetic algorithm is of limited applicability with the exception of systems at a low degree of signal overlap. On the other hand, the recovery of a probe spectrum in the analytical process always gives satisfactory results independent of the features of the probe system. The genetic algorithm obviously shows autoadaptive behavior in probe spectrum recovery. The quality and quantity of the resulting components may dramatically differ from the given probe, although the resulting spectrum is nearly the same. In such cases, the resulting component mixture can be interpreted as an imitation of the probe. As well probe spectra, theoretically designed spectra can also be autoadapted by genetic algorithms. The only limitation is that the desired spectrum must, of course, be incorporated into the search space defined by the involved components. Furthermore, a spectral signal is only one single property of a chemical compound or mixture. Because of the nonlinear search characteristic of genetic algorithms, any other chemical or physical property can also be treated as a desired property. Therefore, the conclusion of the study is well-founded that an old challenge of applied chemistry, namely, the development of new chemical products with desired properties, seems to be reachable under the control of genetic algorithms.