CALIBRATION OF DYE SOLUTIONS WITH ARTIFICIAL NEURAL NETWORKS

In recent years artificial neural networks (ANN) have generated widesp read interest and gained popularity. Numerous applications have been i nvestigated, including pattern recognition, signal processing, process control and modeling or calibrating. ANN as a nonlinear calibration m ethod has been found to give superior results in calibration of nonlin early behaving solutions. In the present study neural networks are app lied to multivariate calibration of dye solutions using spectroscopic data and for producing quantitative estimates of the concentrations of a component in the mixture. Absorbance is linearly dependent on conce ntration only in a narrow concentration range. There exists severe ove rlapping of the absorption bands in multi-component dye solutions. The solutions studied here were multi-component and the concentration ran ge of the dye to be calibrated was wide (0-975 mg/l). Thus the absorba nce of the solutions changes nonlinearly as a function of concentratio n. The results are compared with the results obtained with other multi variate calibration methods; principal component regression and partia l least-squares regression. Metal complex dyes were used in making the dye solutions. The absorbance spectra of the samples were measured in the visible range of light. The sigmoid output function was used in t he hidden layer of the network to perform nonlinear fitting.