Machine learning prediction and calibration of cellulose-based solid-phase extraction performance for pharmaceuticals across aqueous matrices

Key Points

• The research aims to enhance the understanding of cellulose-based solid-phase extraction efficiency for pharmaceuticals in various aqueous environments.
• Utilized machine learning algorithms for predicting extraction performance.
• Examined the effects of different matrix chemistries and sorbent functionalization.
• Analyzed various extraction formats and elution strategies.
• Identified significant variability in extraction performance across different aqueous matrices.
• Machine learning models indicated potential for accurate performance prediction with proper calibration.