Spectrophotometric determination of synthetic colorants using PSO-GA-ANN

- The hybrid of PSO and GA has been suggested to obtain weights and biases of ANN.
- The proposed method can give appropriate solutions without trapping in local optimum solution.
- The applied algorithm is able to model the nonlinear absorbance-concentration relationship and there is no need to use extracted significant principal components as input of ANN.
- ANN-PSO-GA method is a powerful tool to estimate the concentration of food colorants with a high degree of overlap without any prior separation.

Four common food colorants, containing tartrazine, sunset yellow, ponceau 4R and methyl orange, are simultaneously quantified without prior chemical separation. In this study, an effective artificial neural network (ANN) method is designed for modeling multicomponent absorbance data with the presence of shifts or changes of peak shapes in spectroscopic analysis. Gradient descent methods such as Levenberg-Marquardt function are usually used to determine the parameters of ANN. However, these methods may provide inappropriate parameters. In this paper, we propose combination of genetic algorithms (GA) and partial swarm optimization (PSO) to optimize parameters of ANN, and then the algorithm is used to process the relationship between the absorbance data and the concentration of analytes. The hybrid algorithm has the benefits of both PSO and GA techniques. The performance of this algorithm is compared to the performance of PSO-ANN, PC-ANN and ANN based Levenberg-Marquardt function. The obtained results revealed that the designed model can accurately determine colorant concentrations in real and synthetic samples. According to the observations, it is clear that the proposed hybrid method is a powerful tool to estimate the concentration of food colorants with a high degree of overlap using nonlinear artificial neural network.