Modelling of solid-phase tea waste extraction for the removal of manganese from food samples by using artificial neural network approach

In this study, a three-layer artificial neural network (ANN) model was employed to develop prediction model for removal of manganese from food samples using tea waste as a low cost adsorbent. After removal of manganese from food samples with acetic acid (5 mol L−1), manganese was adsorbed to a small amount of tea waste, desorbed with nitric acid as a eluent solvent, and determined by flame atomic absorption spectrometry. The input parameters chosen of the model was pH, amount of tea waste, extraction time and eluent concentration. After backpropagation (BP) training, the ANN model was able to predict extraction efficiency of manganese with a tangent sigmoid transfer function at hidden layer and a linear transfer function at output layer. Under the optimum conditions, the detection limit was 0.6 ng g−1. The method was applied to the separation, pre-concentration and determination of manganese in food samples and one reference material.