Exploiting the versatility of vacuum‐assisted headspace solid‐phase microextraction in combination with the selectivity of ionic liquid‐based GC stationary phases to discriminate Boswellia spp. resins through their volatile and semivolatile fractions

Abstract

fast gas chromatography, ionic liquids, resins, stationary phases

The effect of vacuum: an emerging experimental parameter to consider during headspace microextraction sampling

“…Moreover, depending on the microextraction method used, the air is removed with the extracting phase exposed to the gas phase (e.g., the coated stir bar in Vac-HSSE), and this setup was not found to affect extraction [24]. Removing the air in the presence of the sample should not affect the extraction of less volatile analytes but can lead to losses of the more volatile analytes due to aspiration [14,17]. This drawback can be effectively overcome if an appropriate strategy is followed.…”

“…This step can be optimized if a vacuum gauge is connected to the vacuum line and the minimum evacuation time can be determined. Another point to consider is to freeze the sample before air evacuation [17,30]. This will decrease analyte concentration in the headspace and minimize the portion of volatile analytes aspired during air evacuation.…”

“…It is noted that freezing of the sample should be done immediately after introducing the sample, so that analytes do not have enough time to equilibrate with the headspace. Recently, Capetti et al reported that increasing the sample amount next to freezing a solid sample is an effective strategy to minimize aspiration of volatile analytes [17]. In their work, the authors concluded that problems with analyte losses recorded with 5 mg of frankincense sample could be overcome when using 100 mg of sample instead.…”

“…In 2012, Psillakis et al presented the theoretical model describing the pressure dependence of the so-called vacuum-assisted HS-SPME (Vac-HS-SPME) for water [12,13], and in 2015, for solid matrices [14]. Vac-HS-SPME has been successfully applied to a variety of analytes and matrices including solids [15][16][17] and complex food matrices like wine [18], dairy products [19,20], and extra virgin olive oil [21]. In 2017, the applicability of the approach was expanded by Trujillo-Rodríguez et al to headspace SDME (HS-SDME) and the resulting method, termed vacuum-assisted HS-SDME (Vac-HS-SDME), yielded shorter equilibration times for short-chain free fatty acids compared with atmospheric pressure sampling [22].…”

Effects of different drying methods on the chemical constituents of Lilium lancifolium Thunb. based on UHPLC‐MS analysis and antidepressant activity of the main chemical component regaloside A

“…The data mining of fingerprint information and bioactivity research can find the stable characteristic components in Chinese medicinal materials, which can make the quality and efficacy of TCM be associated, and help to clarify the mechanism of TCM. At present, the main methods to study the fingerprint of TCM are LC (UHPLC/high-performance liquid chromatography), gas chromatography, and infrared spectroscopy [6,7]with extraction processes, such as presoaking, liquid ammonia pretreatment, and others [8][9][10]. Among them, the UHPLC method has displayed its advantages such as simple operation, and high speed and sensitivity [11].…”