Multivariate regression and artificial neural network to predict phenolic content in Schinus terebinthifolius stem bark through TLC images

This study aims to determine a suitable artificial neural network (ANN) regression model for quantification of polyphenols from herbal matrices through thin-layer chromatography (TLC) images. Gallic acid was used as a standard for the development and evaluation of this analytical approach. Analyses were performed on samples extracted from the stem bark of Schinus terebinthifolius, an important species in traditional Brazilian medicine. Calibration curves were obtained by TLC for image acquisition and assessment. High-performance liquid chromatography was used to verify the relationship between the data. The image features were associated with artificial intelligence techniques to determine an ANN configuration. This approach provides an innovative and low-cost analytical strategy with satisfactory performance for the prediction of phenolic content through TLC plate images.