Use of neural networks in solving interferences caused by formation of intermetallic compounds in anodic stripping voltammetry

Abstract

Abstract Neural networks (NNs) were used to overcome interferences by formation of intermetallic compounds in anodic stripping voltammetry (ASV). The software developed for this purpose allows one to construct NNs of virtually any type of architecture and train it automatically using the back‐propagation method. The ability of NNs for addressing interferences arising from interactions with the hanging mercury drop electrode is demonstrated for the Cu–Zn binary system.