Prediction of the xanthine oxidase inhibitory activity of celery seed extract from ultraviolet–visible spectrum using machine learning algorithms

Abstract

Abstract: Xanthine oxidase inhibitory activity of celery seed extract was predicted from UV–Vis spectrum using five different algorithms: multiple linear regression, artificial neural network, support vector regression, random forest and partial linear regression. The optimal parameters of each model established by these methods were examined carefully and the best model was selected. The results showed that the model obtained by partial linear regression had the best quality with coefficient of determination R2 = 0.9618, correlation coefficient of leave-one-out cross validation Q2 = 0.8746, average absolute deviation MAD = 0.0671, root mean square error RMSE = 0.0761 and the accuracy of the model with train and test were 99.29% and 96.69%, respectively. Our findings suggest that this model could be an effective method for rapidly determining xanthine oxidase inhibitory activity of herbal medicine, particularly celery seed extract from UV–Vis spectra. However, more investigations are required in order to improve the performance of the partial linear regression model. Graphic abstract: [Figure not available: see fulltext.].