The headspace analysis of some dairy products has been evaluated using a new sample preparation technique called “Vacuum Assisted Sorbent Extraction” (VASE) that improves the recovery of heavier and more polar volatile compounds. After introducing a sample into a 20-40mL vial, a cartridge containing 70mg of Tenax is placed into the headspace of the vial usinga vacuum tight interface that allows the vial headspace to be evacuated to less than 0.01 atm, or at least until the pressure needed to boil an aqueous mixture at 25 deg C. This results in faster diffusion from the sample/headspace boundary layer to the adsorbent, enhancing the rate of sample extraction. In particular, heavy volatile compounds with low vapor pressures that have little to no response by classical SPME are extracted 10-50x more efficiently. Unlike Dynamic Headspace that pushes volatiles from a sample through the adsorbent bed, VASE vacuum extraction is done statically which deposits the sample much closer to the entrance of the adsorbent bed, providing much better recovery of heavier compounds, and far less carryover. Once placed under vacuum, the extraction is allowed to proceed for between minutes to many hours, or even up to 24 hours, to effect complete, reproducible extractions. In the analysis of dairy products where headspace volatiles are generally at low concentrations, VASE extraction is allowed to proceed for 4-24 hours, with many Sorbent Pens collecting in parallel to allow many samples to be prepared simultaneously to accommodate the production laboratory. The increase in sample extraction duration combined with a large phase to sample ratio allows more accurate determination of headspace composition, with reduced matrix affects. Data is presented showing milk and cheese analysis, with recovery of compounds well into the semi-volatiles range.

Quantitative Headspace Measurement of Volatiles in Dairy Products using Vacuum Assisted Sorbent Extraction (VASE) & GCMS Analysisopen  Download 
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Create DateApril 18, 2017
Last UpdatedJuly 13, 2017