Spatially Resolved Headspace Extractions of Trace-Level Volatiles from Planar Surfaces for High-Throughput Quantitation and Mass Spectral Imaging.

The use of headspace thin-film microextraction devices (SPMESH) for parallel extraction of trace-level volatiles prior to direct analysis in real-time mass spectrometry (DART-MS) has been reported previously, in which volatiles were extracted from samples in multi-well plates. In this report, we demonstrate that headspace extraction of volatiles by SPMESH sheets can be performed directly from planar surfaces. When coupled with DART-MS, this approach yields volatile mass spectral images with at least 4 mm resolution.

Parallel Headspace Extraction onto Etched Sorbent Sheets Prior to Ambient-Ionization Mass Spectrometry for Automated, Trace-Level Volatile Analyses.

Headspace (HS) extraction and preconcentration of volatiles by solid-phase microextraction (SPME) can improve the sensitivity and selectivity of ambient ionization-mass spectrometry approaches like direct analysis in real time (DART), but previous approaches to HS-SPME-DART-MS have been challenging to automate. This report describes the production of inexpensive, reusable solid-phase mesh-enhanced sorption from headspace (SPMESH) sheets by laser-etching mesh patterns into poly(dimethylsiloxane) (PDMS) sheets. Parallel headspace extraction of volatiles from multiple samples can be achieved by positioning the SPMESH sheets over multiwell plates and then attaching to a positioning stage for automated DART-MS quantitation.

Trace-Level Volatile Quantitation by Direct Analysis in Real Time Mass Spectrometry following Headspace Extraction: Optimization and Validation in Grapes.

Ambient ionization mass spectrometric (AI-MS) techniques like direct analysis in real time (DART) offer the potential for rapid quantitative analyses of trace volatiles in food matrices, but performance is generally limited by the lack of preconcentration and extraction steps. The sensitivity and selectivity of AI-MS approaches can be improved through solid-phase microextraction (SPME) with appropriate thin-film geometries, for example, solid-phase mesh-enhanced sorption from headspace (SPMESH). This work improves the SPMESH-DART-MS approach for use in food analyses and validates the approach for trace volatile analysis for two compounds in real samples (grape macerates).