“The SPME-SIFT-MS method is a fast, valuable non-destructive alternative to monitor selected VOCs in the headspace of packages that could serve as potential biomarkers.”

William Kerr Application Scientist

Dr. Will Kerr

BSc(Hons), PhD

Environmental Technical Specialist

There is no “one size fits all” approach to sampling volatile organic compounds (VOCs). Methodologies are selected to address new objectives in a manner appropriate to the analyte and, for this reason, the chosen quantitation technique must be compatible with a variety of sampling platforms. A recent publication by researchers at Ghent University in Belgium highlights the utility of selected ion flow tube mass spectrometry (SIFT-MS) in this role.

Angelos-Gerasimos Ioannidis and his co-workers successfully implemented SIFT-MS interfaced with solid-phase microextraction (SPME) sampling to provide a SPME-SIFT-MS methodology with which to monitor the evolution of microbial VOCs in the headspace of chicken meat stored in modified atmosphere packaging (MAP). Such VOCs are responsible for the odor of spoiled meat and monitoring them over the product’s lifetime would shed valuable insight into the spoilage of meat under MAP-conditions. The full article can be found here.

Measuring the headspace VOC concentrations non-destructively was essential to ensuring that meaningful concentration profiles could be recorded over a two-week period. Sampling at regular intervals was achieved by inserting a SPME fiber through a septum in the packaging, allowing VOCs to be adsorbed without compromising the MAP atmosphere. A thermal desorption injector system coupled to the SIFT-MS was then used to desorb the SPME fiber and quantify the target VOCs. As such, SIFT-MS was conveniently adapted for the non-destructive SPME-SIFT-MS protocol, providing concentration profiles for key spoilage biomarkers including alcohols, ketones and sulfur compounds.

For more information on the integration of sampling methods with SIFT-MS, visit our website or contact us at info@syft.com.

Written by Will Kerr, Applications Scientist