Cross-column density functional theory-based quantitative structure-retention relationship model development powered by machine learning.

Quantitative structure-retention relationship (QSRR) modeling has emerged as an efficient alternative to predict analyte retention times using molecular descriptors. However, most reported QSRR models are column-specific, requiring separate models for each high-performance liquid chromatography (HPLC) system. This study evaluates the potential of machine learning (ML) algorithms and quantum mechanical (QM) descriptors to develop QSRR models that can predict retention times across three different reversed-phase HPLC columns under varying conditions. Four machine learning methods-partial least squares (PLS) regression, ridge regression (RR), random forest (RF), and gradient boosting (GB)-were compared on a dataset of 360 retention times for 15 aromatic analytes. Molecular descriptors were calculated using density functional theory (DFT). Column characteristics like particle size and pore size and experimental conditions like temperature and gradient time were additionally used as descriptors. Results showed that the GB-QSRR model demonstrated the best predictive performance, with Q2 of 0.989 and root mean square error of prediction (RMSEP) of 0.749 min on the test set. Feature analysis revealed that solvation energy (SE), HOMO-LUMO energy gap (∆E HOMO-LUMO), total dipole moment (Mtot), and global hardness (η) are among the most influential predictors for retention time prediction, indicating the significance of electrostatic interactions and hydrophobicity. Our findings underscore the efficiency of ensemble methods, GB and RF models employing non-linear learners, in capturing local variations in retention times across diverse experimental setups. This study emphasizes the potential of cross-column QSRR modeling and highlights the utility of ML models in optimizing chromatographic analysis.

3D-printed extraction devices fabricated from silica particles suspended in acrylate resin.

In recent years, there has been an increasing worldwide interest in the use of alternative sample preparation methods. Digital light processing (DLP) is a 3D printing technique based on using UV light to form photo-curable resin layer upon layer, which results in a printed shape. This study explores the application of this technique for the development of novel drug extraction devices in analytical chemistry. A composite material consisting of a photocurable resin and C18-modified silica particles was employed as a sorbent device, demonstrating its effectiveness in pharmaceutical analysis. Apart from estimating optimal printing parameters, microscopic examination of the material surface, and sorbent powder to resin ratio, the extraction procedure was also optimised. Optimisation included the type and amount of sample matrix additives, desorption solvent, sorption and desorption times, and proper number of sorbent devices needed in extraction protocol. To demonstrate this method's applicability for sample analysis, the solid-phase extraction followed by gas chromatography coupled with mass spectrometry (SPE-GC-MS) method was validated for its ability to quantify benzodiazepine-type drugs. This evaluation confirmed good linearity in the concentration range of 50-1000 ng/mL, with R2 values being 0.9932 and 0.9952 for medazepam and diazepam, respectively. Validation parameters proved that the presented method is precise (with values ranging in-between 2.98 %-7.40 %), and accurate (88.81 % to 110.80 %). A negative control was also performed to investigate possible sorption properties of the resin itself, proving that the addition of C18-modified silica particles significantly increases the extraction efficiency and repeatability. The cost-effectiveness of this approach makes it particularly advantageous for single-use scenarios, eliminating the need for time-consuming sorbent-cleaning procedures, common in traditional solid-phase extraction techniques. Future optimisation opportunities include refining sorbent size, shape, and geometry to achieve lower limits of quantification. As a result of these findings, 3D-printed extraction devices can serve as a viable alternative to commercially available SPE or solid-phase microextraction (SPME) protocols for studying new sample preparation approaches.