Fragrances are important characteristics of food and wellness products. Understanding the science behind fragrances remains a complex challenge, as a multitude of odorant molecules act together to give a characteristic fragrance.

Advanced chromatographic methods have widely been used to identify volatile odorants from different sources, and analytical methods are continuously advancing to give improved insights.

Researchers from Italy, Germany, and the USA reviewed state of the art of odorant analysis with multidimensional gas chromatography (MD-GC) coupled to serial detection systems, providing deep insights into what makes a pleasant fragrance.

This review was published in the journal Trends in Analytical Chemistry.

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Ever smelled on a food product before making the decision to purchase? The fragrance, or aroma of our food is key to whether we will find it pleasant or not. The science behind fragrances is surprisingly complex, as usually tenths or hundreds of volatile compounds define together what we perceive as a smell. However, instead of smelling the most concentrated compounds: in fact, many odorants are perceived as pleasant at rather low concentrations, and lose their appeal at high concentrations.

Analyzing volatile compounds that make up a characteristic aroma thus becomes a challenge for analytical scientists, as they have to determine compounds at largely different concentrations and with high specificity.

For chromatographic techniques, this imposes a major challenge, as it renders it likely that molecules with similar chemistry are challenging to separate, requiring advanced, multidimensional separation techniques coupled with specific and multidimensional detection systems.

A collaboration from Italy, Germany, and the USA, led by Assoc. Professor Chiara Cordero reviewed the recent advances in two- and multidimensional gas chromatography (2D/MD-GC) coupled to various detectors as an opportunity to understand fragrance compositions in detail.

Multidimensional gas chromatography – A pressure challenge

The initial challenge for odorant analyzes is to collect the fragrance molecules. Since many odorants are volatile and have a low water solubility, headspace (gas phase) sampling is commonly carried out, with subsequent trapping of volatile compounds by solid-phase or liquid-phase extraction methods.

Once ready for gas chromatography analysis, column length and stationary chromatography phase are key to separate odorant mixtures. However, a single GC column often fails to separate compounds sufficiently for identification and quantification.

It has thus become common to use multidimensional gas chromatography systems, where, for instance a first gas chromatography column separates compounds based on hydrophobic interaction and the second column based on hydrophilic interaction.

Such 2D methods achieve better separation as they can make use of orthogonal physicochemical features of odorant molecules, and it becomes much less likely that two compounds have e.g. both similar hydrophobic and hydrophilic features.

The challenge of multidimensional gas chromatography is that the timing of the multistage chromatography needs to be suitable.

In multidimensional gas chromatography, samples coming off the column in the first dimension are immediately injected to the second column for separation, implying that the chromatography in the second dimension needs to be fast. This is commonly achieved by using shorter and thinner chromatography columns in the second dimension, balancing separation speed against efficiency. However, this means that pressure-jump can arise at the junction that needs to be controlled.

Once under control though, some researchers have shown that they can be used to actually improve separation efficiency. The review gives further examples of advanced gas chromatography methods, such as the integration of thermal and flow modulators that further improve the separation efficiency of multidimensional gas chromatography systems.

Computer-Aided Interpretation of Multidimensional Detection Data

Beyond the separation of complex odorant mixtures, gas chromatography offers the capability to analyze samples with multiple detection systems simultaneously. For instance, separated samples can be split and measured in different, common detectors such as tandem mass spectrometry (MS/MS) and flame ionization detectors (FID). Obviously, this generates multidimensional detection information that requires computational methods to interpret.

The review authors around Professor Cordero, therefore, review chemometric statistical methods to combine multidimensional data and identify compound profiles characteristic for different fragrances automatically.

Methods that have been employed comprise clustering and regression algorithms along with principal component analysis, in order to identify compounds based on physicochemical parameters measured by different detectors, and simultaneously read out the key compound and concentration profiles (i.e. principal components) from sample mixtures.

There have been without doubt impressive advances in the analysis of complex fragrance mixtures, and the field is still advancing towards improved compound separation and data analysis methods. The automation efforts in computer-aided data interpretation of multidimensional chromatography data bears huge promises in making such analyzes available for more routine work, aiding to understand and authenticate fragrance mixtures from different sources and products.

Further Reading

• All Chromatography Content
• Chromatography Overview
• Gas Chromatography-Mass Spectrometry (GC-MS) Applications
• High Performance Liquid Chromatography (HPLC)
• Liquid Chromatography-Mass Spectrometry (LC-MS) Applications

Written by

Christian Zerfaß, Ph.D.

Christian is an enthusiastic life scientist who wants to understand the world around us. He was awarded a Ph.D. in Protein Biochemistry from Johannes Gutenberg University in Mainz, Germany, in 2015, after which he moved to Warwick University in the UK to become a post-doctoral researcher in Synthetic Biology.