Application of artificial neural networks to predictions in flow-injection spectrophotometry

Abstract

It is demonstrated that predictions can be obtained in spectrophotometric flow-injection analysis (FIA) based on an experimental parameter, that is, the degree of reaction, which takes into account the hydrodynamic and chemical characteristics of the spectrophotometric reaction used. The search algorithm is based on constructing a model of a chemical-analytical process using a learning artificial neural network that enables the prediction of the degree of reaction for some reagents not studied yet. The trained neural network is used for the a priori evaluation and comparison of a number of reagents for the determination of aluminum by FIA.