Application of machine learning models for green and simultaneous determination of asiaticoside and madecassoside in Centella asiatica

Abstract

       The artificial neuron network (ANN), which is a subfield of machine learning, has been widely applied in analytical chemistry for classification/pattern recognition, prediction, and modeling. ANN combined with UV spectrometry can be used to tackle the problem of overlapping spectra of a complicated matrix of herbal medicine. In the present study, ANN has been used for simultaneous determination of asiaticoside and madecassoside in Centella asiatica collected from various provinces in Viet Nam based on UV spectra of standard reference and spiked samples. The absorption spectra of 108 C. asiatica samples were recorded in (190-250) nm of wavelength with an interval of 1 nm (61 variables) were used for data acquisition. An ANN model using traincgb function with 40 neural hidden layers was trained. The correlation coefficients were all higher than 0.9999. The concentration of asiaticoside and madecassoside in all tested samples revealed a good recovery, as compared to the referenced values. The ANN model can be considered as effective, time-saving and environment-friendly quantitative analysis tool for herbal medicine