New artificial network model to estimate biological activity of peat humic acids

Abstract

PurposeThis article focuses on new method to estimate biological activity of peat humic acids (HAs) using artificial neural network (ANN) to process spectroscopic measurements in infrared and visible ranges. Conventional approaches generally rely on biological models and direct detection of chemical substances related to bioactivity. These methods proved to be accurate and reliable, but at the expense of speed and simplicity. Materials and methodsRecently, a conception of quantitative structure-activity relationship (QSAR) has been introduced and successfully implemented to predict effects of HAs on toxicity of polycyclic aromatic hydrocarbons. Our research stems from this conception, but employs multilayer perceptron (MLP) model to improve overall performance. The developed MLP model allowed us to estimate biological activity of the complete vertical peat cores collected from oligotrophic peat bog, located in southern taiga zone of West Siberia (north-eastern spurs of the Great Vasyugan Mire, 56°58′ N 82о36’ E). In total, 42 samples taken from the cores were collected. The protocol included spectroscopy (in infrared and visible ranges) and biological model with peritoneal activated macrophages as a reference method to directly measure biological activity of HAs. Resultsand discussion. Numerical experiments confirmed consistency of the measured and estimated bioactivity, coefficient of determination R2 = 0.97. These experiments also showed that the MLP model significantly outperforms conventional linear multiple regression models, mainly due to essential nonlinearity of structure-activity relationships. ConclusionsOur research demonstrates that biological activity of HAs extracted from peat samples can be estimated using an artificial neural network model trained on infrared and visible spectra.