Headspace solid-phase microextraction (HSSPME) sampling under vacuum conditions is a new and effective approach to accelerate the extraction kinetics of analytes with a low affinity for the headspace. Vacuum-assisted HSSPME… Click to show full abstract
Headspace solid-phase microextraction (HSSPME) sampling under vacuum conditions is a new and effective approach to accelerate the extraction kinetics of analytes with a low affinity for the headspace. Vacuum-assisted HSSPME (Vac-HSSPME) evolved from this approach and the resulting methods were always found to yield high extraction efficiencies and very good sensitivities within short sampling times and at mild temperatures. Vac-HSSPME preserves the simplicity of regular HSSPME and the only extra step required is that of air-evacuating the sample container before or after introducing the sample. Moreover, fast implementation of the technique is possible when using the latest, simplified and easy to construct sample container that can hold constant low-pressure conditions for extended sampling times. The main objective of the current tutorial is to provide a general strategy that can be applied towards the development of new Vac-HSSPME methods. The most important outcomes of past theoretical investigations are highlighted and a simple criterion for predicting the effect of vacuum on HSSPME sampling of water or water-containing samples is outlined. This theoretical discussion is then used as a background to elucidate the combined effects of low sampling pressure and several other experimental parameters on HSSPME sampling. Specific implications unique to Vac-HSSPME are also discussed, providing practical tips and a troubleshooting guide to new users. The great benefits of adopting the Vac-HSSPME approach are further demonstrated by reviewing all past applications reporting the quantitative and/or qualitative determination of compounds with a low tendency to escape to the headspace in a variety of samples. Vacuum is a new experimental parameter to control and exploit during HSSPME method optimization. The potential applications of Vac-HSSPME in areas like food, environmental and biological analysis are numerous and still remain to be explored.
               
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