Extraction Yield Prediction for the Large-Scale Recovery of Botanicals

The extraction of compounds from natural sources is essential to organic chemistry, from identifying bioactive molecules for potential therapeutics to obtaining complex, chiral molecule building blocks. Similar to chemical syntheses, anticipating the extraction conditions (flow rate, time, pressure, etc.) that will lead to the highest purity or recovery of a target molecule is difficult. Machine learning algorithms have been demonstrated to streamline reaction optimization processes by constraining the parameter space to be physically tested to predicted regions of high performance; however, it is not altogether clear if these techniques extend to the optimization of extractions where the process conditions are even more expensive to evaluate, limiting the data available for assessment. Combining information from several sources could provide access to the requisite data necessary for implementing a data-driven approach to optimization, but little data has been made publicly available. To address this challenge and to evaluate the capabilities of machine learning for optimizing extraction processes, we built a dataset on the carbon dioxide supercritical fluid extraction (CO2 SFE) of botanicals by harmonizing data from various companies. Using this combinatorial dataset and new techniques for maximizing the information obtained from a single large scale experiment, we built robust machine learning models to accurately predict extraction yields. The resulting machine learning models also allow for the prediction of out-of-sample biomass variations, process conditions, and scales.