30 July 2024
What is gas chromatography olfactometry (GC–O)?
The human nose is an extraordinary
detector for aromas and odours, making it a crucial tool in the flavour and
fragrance industry. While mass spectrometers can identify the compounds present in a mixture, they cannot record their odour characteristics.
Gas chromatography olfactometry (GC–O)
leverages the sensitivity and selectivity of human olfaction by incorporating the human nose as a ‘detector’ in the analytical
system. After separation of the analytes by gas chromatography, human responses
are recorded by ‘sniffing’ at an olfactory detection port (ODP).
When used in
parallel with mass spectrometry, GC–O enables the identification of specific compounds
responsible for perceived odours.
After GC separation, the sample is
split between a detector (e.g. a
mass spectrometer) and the Phaser Pro ODP, where compounds are “sniffed” to record their odour characteristics.
Challenges in gas chromatography olfactometry (GC–O) odour analysis
There’s no doubt that GC–O is an excellent tool for revealing key odorants in a sample, but identifying the ‘sniffed’ compounds for meaningful results presents challenges.
Trace-level odorants: Some compounds have very low odour detection thresholds (ODT), meaning our noses can detect them, but mass spectrometers may struggle.
Co-eluting compounds: In complex food and fragrance samples, coelutions can make it challenging to identify the compound(s) responsible for a perceived odour, as the spectral quality will be impacted by the coelutions.
Thankfully, workflows are available to overcome these challenges and ensure accurate and reliable results.
Sample enrichment techniques for enhanced GC–O detection limits
Sample enrichment techniques can significantly enhance the detectability of low-concentration odorants by increasing the amount of sample transferred to the GC.
The Centri® platform from Markes International can enrich the sample on an electrically-cooled focusing trap prior to transfer to the GC. By using headspace, SPME, or HiSorb™ high-capacity sorptive extraction with the focusing trap, the desorption is decoupled from the injection into the GC. This allows the extraction to be repeated while the analytes are stored on the focusing trap. By repeating the process multiple times, greater sample loadings can be achieved, ultimately allowing more compounds to be detected and identified.
The multi-step enrichment (MSE®) workflow as applied on the Centri platform
For example, in the animation below,
the compound responsible for the “fresh” aroma is initially below the
instrument detection limit of the mass spectrometer, meaning it can be smelled
at the ODP but not identified. By applying multi-step enrichment (MSE), the
sample loading can be increased for confident identification of the compound
responsible for the aroma.
Animation showing how multi-step enrichment can enhance detection limits
for low-concentration odorants that are recorded at the ODP
Resolving coelutions in gas chromatography olfactometry
(GC–O) with GCxGC
Sample complexity is another major
challenge when it comes to identifying the compound(s) responsible for an odour
recorded at a particular timepoint, as coelutions can interfere with the
identification process.
Two-dimensional gas chromatography
(GCxGC) is an enhanced separation technique that has great success in resolving
complex aroma and fragrance profiles. Here, two columns (1D and 2D) containing
different stationary phases are used to provide separation based on two
different chemical properties. For example, in a non-polar to polar column set,
separation is based on volatility and then polarity. In general, GCxGC offers
separation of around 10 times more compounds than 1D GC.
GCxGC provides separation based on two different
chemical properties, resolving compounds that would have coeluted in 1D GC
When coupled with GC-O, GCxGC can physically separate analytes that would have
co-eluted in 1D GC, providing cleaner
spectra and more confident identification of the compound(s) responsible for a
recorded odour. For example, in the animation below, three compounds coelute at
the same timestamp as the “sweet” aroma. In GCxGC, these compounds are
separated in the second dimension for improved characterisation of the sample.
Animation illustrating how GCxGC can resolve coelutions for more
confident identification of the compound(s) responsible for the “sweet” aroma
It is important to note that the
second separation in GCxGC occurs in a matter of seconds, too fast for the
human nose to keep up with. For this reason, the flow is split to the ODP after
the first column, with the remainder of the flow going to the GCxGC modulator
for transfer to the second column.
Here, we use our own INSIGHT-Flow modulator
for efficient and affordable modulation across a wide volatility range, from
very volatiles to semi-volatiles.
Conclusion:
Optimising GC–O workflows for better sensory evaluation
GC–O is an indispensable tool in
sensory evaluation, bridging the gap between chemical analyses and the
subtleties of human olfaction. Understanding smells isn’t just about satisfying
our curiosity; there are real-world
applications, from enhancing food flavours and creating better perfumes to
improving medical diagnostics.
Optimising GC-O workflows is crucial to getting maximum value from the analyses. Techniques like sample enrichment and GCxGC can
elevate GC-O workflows by improving confidence in the identification of
odour-active analytes, making it easier to correlate sensory and chemical data.