Automated microextraction by packed sorbent of endocrine disruptors in wastewater using a high-throughput robotic platform followed by liquid chromatography-tandem mass spectrometry.

An automated microextraction by packed sorbent followed by liquid chromatography-tandem mass spectrometry (MEPS-LC-MS/MS) method was developed for the determination of four endocrine disruptors-parabens, benzophenones, and synthetic phenolic antioxidants-in wastewater samples. The method utilizes a lab-made repackable MEPS device and a multi-syringe robotic platform that provides flexibility to test small quantities (2 mg) of multiple extraction phases and enables high-throughput capabilities for efficient method development. The overall performance of the MEPS procedure, including the investigation of influencing variables and the optimization of operational parameters for the robotic platform, was comprehensively studied through univariate and multivariate experiments.

Modern automated microextraction procedures for bioanalytical, environmental, and food analyses.

Sample preparation frequently is considered the most critical stage of the analytical workflow. It affects the analytical throughput and costs; moreover, it is the primary source of error and possible sample contamination. To increase efficiency, productivity, and reliability, while minimizing costs and environmental impacts, miniaturization and automation of sample preparation are necessary.

Determination of parabens in wastewater samples via robot-assisted dynamic single-drop microextraction and liquid chromatography-tandem mass spectrometry.

Dynamic single-drop microextraction (SDME) was automatized employing an Arduino-based lab-made Cartesian robot and implemented to determine parabens in wastewater samples in combination with liquid chromatography-tandem mass spectrometry. A dedicated Arduino sketch controls the auto-performance of all the stages of the SDME process, including syringe filling, drop exposition, solvent recycling, and extract collection. Univariate and multivariate experiments investigated the main variables affecting the SDME performance, including robot-dependent and additional operational parameters.