Chemometrics for Analytical Data Mining in Separation Process Design for Recovery of Artemisinin from Artemisia annua

Abstract

The separation process for recovery of natural products from plants very often employs multiple separation techniques, and key to the success of such processes is to find the synergy between different separation techniques. Molecular level understanding of process streams is highly required in order to determine the synergy between unit operations, which can be attained through analysis of process streams using advanced process analyzers such as high performance liquid chromatography (HPLC), liquid chromatography–mass spectrometry (LC-MS), etc. Very often use of such process analyzers generates an enormous amount of data making it difficult to extract useful information. Therefore, application of chemometrics for extracting process information from analytical data of process streams is demonstrated in this work. The multivariate data analysis technique PARAFAC is used to extract chemical information such as number of impurities, their relative concentrations, and finally their identification from rather complex analytical chromatograms of flash column chromatography (flash CC) fractions during purification of artemisinin from the crude extract of Artemisia annua. Crude extract of A. annua leaves obtained from dichloromethane is used in this work. Prior to the application of PARAFAC, the data set is preprocessed to remove baseline drift and peak misalignment caused by retention time shifts due to matrix effects. The process information extracted from analytical chromatograms by using the PARAFAC technique indicated the presence of impurities ranging from coumarins, polyacetylenes, and flavonoids to artemisinin related compounds in the flash CC fractions.