The Use of SPME and GC×GC in Food Analysis: An Interview with Giorgia Purcaro

The food analysis industry has benefitted from advanced techniques such as solid-phase microextraction (SPME) and comprehensive multidimensional gas chromatography (GC×GC). SPME allows for high enrichment factors despite the matrix and, coupled with multidimensional GC instrumentation, has allowed for outstanding versatility, including in food analysis (1).

Giorgia Purcaro is exploring this question. Purcaro teaches analytical chemistry at Gembloux Agro-Bio Tech at the University of Liège in Belgium (2). LCGC International recently sat down with Purcaro to talk about her team’s latest research in this space (3).

Giorgia Purcaro of Gembloux Agro-Bio Tech at the University of Liège. Photo Credit: © Giorgia Purcaro

SPME and GC×GC are described as milestone innovations in separation science. In your view, what key factors made their coupling so transformative for food analysis?

Both techniques are compelling. SPME allows for an outstanding enrichment factor without any sophisticated tool or cumbersome procedure. On the other hand, GC×GC, characterized by an incredible separation power, can sort and separate an astonishing number of compounds. These two complementary characteristics make their coupling so perfect. In all investigations, you need to collect as much data as possible, but only when you reorganize and structure the results can the valuable information be extracted to provide a rational answer to the initial scientific question. SPME represents the tool to trap compounds easily, thanks to its outstanding enrichment factor; meanwhile, GC×GC can sort and organize them, generating what can be called a chromatographic fingerprint. The latter facilitates the interpretation of the data.  

How has the shift from targeted analysis to a more holistic approach in food quality assessment impacted the detection and understanding of food contaminants and other markers?

Nothing is an isolated entity; everything is interconnected in this world. This is for any consideration and any field you may consider. It is the same in food analysis. Looking at a single constituent, the big picture is lost. A clear example is food contaminants related to the non-intended added substances (NIAS), which refer to impurities present in food contact materials but not added for any technical reason, which may have a more significant impact than the substance controlled with the targeted method. Only untargeted approaches allow us to study them.

Moreover, what is interesting about the untargeted approach is that the data are collected without any bias, so they remain available, and there is the possibility of post-targeted analysis. Everything said is true also for markers of quality. The entire volatile aroma represents a fingerprint of the product, encrypting all the information regarding its story. We only need the right key to interpret this information.

Foodomics (comprehensive analysis of food components) plays an increasing role in food analysis. Can you elaborate on how SPME-GC×GC–MS contributes to this field, and what new insights it offers compared to traditional techniques?

Foodomics goes beyond the comprehensive analysis of food components; it recognizes the central role of food in human health and well-being and tries to understand the hidden relationships. In this regard, SPME-GC×GC generates information-rich chromatographic fingerprints from which the needed information can be extrapolated. Well said like this, it may seem very easy, but it is not! It is necessary to ask a good question and have the right tools to decrypt this information!

What are some of the most notable applications of SPME-GC×GC in food analysis, and how have they improved the accuracy and depth of volatile and semi-volatile analyte detection?

Several exciting works have been published recently, but if I need to consider the most notable ones, two main aspects are of high interest to maximize the exploitation of the SPME-GC×GC marriage, the enhancement of SPME's ability to trap semi-volatiles and the introduction of artificial intelligence (AI) tools in support of the data handling of SPME-GC×GC data. The former can be achieved by exploiting vacuum-assisted SPME, a powerful technique that increases the kinetic, thus enhancing the semi-volatile extraction without impacting the more volatile. As a result, a much richer and more informative profile is obtained, which benefits even more from the coupling with GC×GC. Similarly, the use of thin-film extraction (TFE) and the coupling of TFE and SPME allows for a tunable coverage of the volatile and semi-volatile. On the other side, the introduction of artificial intelligence (AI) tools in support of the data handling of SPME-GC×GC data plays a crucial role in making functional use of the increased amount of data generated from the enhancement of the techniques. Within the latter topic, the most notable works from my viewpoint are produced by the group of Professor Chiara Cordero.

The study emphasizes the importance of appropriate data treatment. Could you discuss some challenges associated with handling the complex data sets produced by SPME-GC×GC and the solutions to overcome these challenges?

Appropriate data treatment of the chromatographic fingerprints is a multiple-layer topic that highly requires a collaborative approach to master the different aspects involved in the process entirely. The process indeed is composed of three main steps: data generation; data processing; and data mining. In the first step, mastering the technique (in this case, SPME-GC×GC) is fundamental to generating robust and significant data. As an intermediate step, the massive amount of data generated chromatographically by GC×GC–MS needs to be processed. In this context, many approaches can be used, such as peak features methods, pattern recognition, and computer vision (tile and visual features) methods. Finally, the data matrices generated need to be treated through advanced chemometrics approaches. In this regard, the major challenge is that, unlike spectroscopic data, where sample numerosity and features are balanced, GC×GC–MS has a significant asymmetry between the informative density and the sample size.

To maximize the information obtained from such a highly informative technique as SPME-GC×GC–MS and to properly perform the process mentioned above, a high level of expertise is required at each step, so it highly benefits from collaborations between analytical chemists and chemometrics.

In what ways do you think future developments in multidimensional chromatography and data analysis will further enhance food quality assessment and foodomics research?

I believe food can be considered a form of personalized medicine for the body and the soul, but it can also represent a significant threat to human health. However, the complexity of its implications is still far from being disclosed. Foodomics is trying to understand this complex relationship. Only a holistic approach, where multidimensional techniques represent a key player in sorting and rationalizing the data, further supported by robust data handling, can provide the key to understanding the complexity of foodomics and guaranteeing food quality.

How has the integration of these advanced analytical techniques affected the broader food industry, especially in terms of food safety, authenticity, and regulatory compliance?

Unfortunately, I feel that only the most innovative industries understand the decisive role that these tools can play. I would like to see much more diffusion in routine and industrial applications. They can simplify the whole workflow, making it greener while providing way more information in a shorter time. Often, I hear comments like, “it is too difficult,” “we have always done this way,” and “we go on like this.” Although very recently, some shiny signals of change can be perceived, for instance, the clear mention by the European Food Safety Authority of the use of GC×GC to provide more robust results for characterizing food contaminated with mineral oil hydrocarbons.

What advice would you give to researchers who are new to working with SPME-GC×GC–MS in food analysis, particularly regarding the design of experiments and data interpretation?

They are powerful tools and easy to use, but never disregard the basis to avoid blunders and obtain remarkable results from their combination!

References

1. Aspromonte, J.; Lancioni, C.; Purcaro, G. Solid-Phase Microextraction—Gas Chromatography Analytical Strategies for Pesticide Analysis. Methods Protoc. 2022, 5 (5), 82. DOI: 10.3390/mps5050082
2. University of Liege, Giorgia Purcaro. University of Liege. Available at: https://www.uliege.be/cms/c_10548111/en/giorgia-purcaro (accessed 2024-11-07).
3. Aspromonte, J.; Mascrez, S.; Eggermont, D.; Purcaro, G. Solid-phase Microextraction Coupled to Comprehensive Multidimensional Gas Chromatography for Food Analysis. Anal. Bioanal. Chem. 2024, 416, 2221–2246. DOI: 10.1007/s00216-023-05048-0