FT NIR assisted Machine and Deep learning for determination of Acteoside, Aucubin and Catalpol contents of Plantago lanceolata

Aucubin, acteoside and catalpol are important compounds present in this plant but their determination is time consuming, costly and necessitate the use of chemicals. In the present study we developed a method based on FT-NIR spectroscopy to determine aucubin, acteoside and catalpol from dry powders of Plantago lanceolata. FT-NIR spectra were processed in PLS and deep learning methods and the accuracies of models were calculated and compared. Aucubin, acteoside and catalpol contents were predicted with a root mean square error of 0.56, 0.25 and 0.18%, respectively by using PLS method. Deep learning did not allow to improve the accuracies but allowed obtaining similar accuracies. FT-NIR based method showed interesting results and is promising to develop a fully usable method for selection programs of plantain with high level contents of bioactive compounds.