Predictive Analytics in Phytochemistry

Abstract

This chapter presents a data-driven framework for predicting the extraction yield of bioactive compounds from Opuntia ficus-indica cladodes using machine learning techniques. Plant materials were collected from three geographically distinct Moroccan provinces and subjected to chemical extraction using solvents of varying polarity. A Random Forest regression model was developed using solvent type, province of origin, and quantified phytochemical content as input variables. The model demonstrated high predictive accuracy (R2 ≈ 0.99), with solvent type emerging as the most influential factor, followed by flavonoid and flavonol concentrations. The findings underscore the potential of predictive modeling to optimize experimental design, reduce resource consumption, and guide solvent and region selection in phytochemical research. This chapter provides insights into how supervised learning algorithms can be effectively applied to natural product data to support green extraction processes and enhance reproducibility in phytochemical workflows.