Evaluation of natural computation techniques in the modelling and optimization of a sequential injection flow system for colorimetric iron(III) determination

The present study shows and gives evidence of the applicability of natural computation techniques in the modelling and optimization of a sequential injection flow system of analysis for colorimetric iron(III) determination in water samples. The reaction with thiocyanate is used as reagent colour. A neural network consisting of two hidden layers, each one formed by eight neurons, was used to model the system. Optimization of the system in terms of sensitivity, linearity and sampling rate was carried out by using jointly the neural network and genetic algorithms. The latter were used with a set of 50 crossed and mutated chromosomes over 100 generations. In the system thus developed, 140 μl of sample and 70 μl of reagent were sequentially introduced into the holding coil and propelled toward the detector at a flow of 5 ml/min. The system gave a sampling rate of 110 samples per hour. A comparison of the results obtained in the analysis of six samples with those obtained using the reference method (atomic absorption spectrophotometry) showed the high quality of results provided.