In the realm of bioanalysis, the selection of suitable extraction solvents is a critical factor in ensuring accurate results. A new study has harnessed the power of artificial neural networks (ANNs) to predict the ideal solvent for analyte extraction, ultimately streamlining the sample preparation process.
Liquid-liquid extraction (LLE) is a key technique in bioanalysis, offering high selectivity and simplicity in separating sample components. However, the traditional LLE method can be time-consuming and require large amounts of organic solvents. By utilizing ANNs to predict the most suitable extraction solvent for a given analyte, researchers can significantly reduce the time and effort involved in solvent selection.
Hansen Solubility Parameters (HSPs) play a crucial role in this process, allowing for the prediction of solute-solvent interactions based on their respective energy properties. By inputting a set of analyte descriptors into the ANNs model, researchers can accurately predict the corresponding HSPs of the optimal extraction solvent.
To validate the efficacy of the ANNs model, twenty diverse drugs were extracted from human plasma using the predicted solvent combinations, followed by quantitative analysis using HPLC/UV methods. The results demonstrated the model’s ability to accurately predict suitable extraction solvents, underscoring its potential to revolutionize bioanalytical procedures.
This innovative approach not only enhances the efficiency and accuracy of bioanalytical methods but also aligns with the principles of green chemistry by reducing the consumption of organic solvents. By leveraging the power of machine learning algorithms, researchers can streamline the sample preparation process and ensure robust results in bioanalysis.
