Optimization of vacuum assisted heat reflux extraction process of radix isatidis using least squares-support vector machine algorithm

An improved vacuum-assisted heat reflux extraction (VAHRE) technique was proposed and applied to extract Radix Isatidis. The extraction parameters of VAHRE, including the boiling temperature, extraction cycles, extraction time, soak time, and liquid-solid ratio, were carefully optimized with a single factor experiment. The least-squares support vector machine (LS-SVM) novel machine learning algorithm was introduced to improve the optimization's predictive accuracy. The results indicated that the LS-SVM model had better performance, with a higher R2 and lower RMSE value than those of the conventional quadratic polynomial model (QPM). Compared with conventional reference extraction methods, the VAHRE method gave a higher extraction yield due to indirubin's reduced for volatile loss and more efficient release from the plant matrix with the aid of a vacuum. This is the first study on optimizing the extraction process of Radix Isatidis using VAHRE and LS-SVM algorithms. The present findings also demonstrated that VAHRE was a promising procedure for extracting indirubin from Radix Isatidis, which shows great potential for becoming an alternative system for industrial scale-up applications.