ChromSoc 2025

ChromSoc 2025 showcased the latest advances in Gas Chromatography (GC) and GC–Mass Spectrometry (GC–MS). From automation and novel sample introduction techniques to next generation instrumentation and Artificial Intelligence (AI) driven data processing.

The conference highlighted how AI is increasingly being applied to address long standing analytical challenges: complex sample matrices, overlapping peaks, difficult separations, and the push towards ever-lower detection limits.

There were 10 talks, each offering a glimpse into where GC research is heading. Below, I’ve included short summaries of the presentations and links for further reading.

The opening talk provided a concise overview of the current landscape of GC and GC-MS research. Recent publications can broadly be divided into four key areas:

1. Multidimensional GC and High-Resolution Mass Spectrometry (HRMS)

2. Green and Sustainable GC

3. Automation, Software, and AI/ML – including diagnostics, deconvolution, and data management

4. Miniaturisation and Portability

It’s clear there remains significant scope for innovation across all these domains. In particular, discussions around data handling, diagnostics, GC×GC, and sample introduction (split/splitless inlets for high vs. low concentration samples) highlighted the balance between analytical precision and practicality.

Sustainability also featured prominently, from using greener carrier gases to improving energy efficiency in heating systems. One particularly reflective question posed by a speaker captured the spirit of the event:

“Is heating a column in a large box of air really the way forward?”

It was a fitting reminder that even in a mature technique like GC, there’s always room to rethink fundamentals.

1 – High-Performance, Web-Based Computer Modelling Tools for Simulating Gas and Liquid Chromatographic Separations Using Artificial Intelligence

(Jaap de Zeeuw, CreaVisions)

Jaap de Zeeuw is widely regarded for his deep expertise in gas chromatography, supported by decades of hands on experience, numerous patents, and continuous contributions to method development. In this talk, he presented his latest work: an AI-driven approach to simulate chromatographic separations for both GC and LC.

The system uses chromatographic databases and compound properties to model expected separations, allowing users to predict retention behaviour, optimise conditions, and explore column configurations in silico before conducting real-world experiments. This has the potential to substantially reduce trial-and-error in method development.

Although Jaap frequently appears at conferences and delivers training through CreaVisions, an official web page for his company is surprisingly difficult to locate. Nonetheless, he shared several valuable modelling and method development tools during his talk.

One resource of particular interest is the Restek EZGC suite, which provides:

• GC method translators

• Column and flow calculators

• A method translator for LC workflows

You can explore these tools here:

EZGC Method Translator & Flow Calculator: https://ez.restek.com/ezgc-mtfc

2 – Trace Analysis of Nitrosamine Precursors by GC–MS and GC–VUV

(Sam Whitmarsh, CatSci, Cardiff)

In 2019, regulatory concern over N-nitrosodimethylamine (NDMA) contamination in Valsartan prompted both the FDA (Food and Drug Administration) and EMA (European Medicines Agency) to reassess nitrosamine risks across pharmaceutical manufacturing. NDMA is classified as a probable human carcinogen, and the ICH (International Council for Harmonisation of Technical Requirements for Pharmaceuticals for Human Use) now requires NDMA levels in active pharmaceutical ingredients (APIs) to be <0.03 ppm, following a two-year transition period. These strict limits demand highly sensitive and robust analytical methods for both detection and quantification.

Sam Whitmarsh presented CatSci’s work on combining GC–MS and GC–VUV for identifying nitrosamine precursors. VUV detection measures the unique absorbance fingerprints of molecules in the vacuum ultraviolet region (125–240 nm), enabling powerful qualitative capabilities even in highly complex matrices such as petroleum products. Traditional detectors such as FID and MS often struggle with overlapping peaks, long run times, or insufficient qualitative information; VUV overcomes many of these challenges by allowing faster separations, higher flow rates, and class-specific spectral identification.

For nitrosamines, sub ppm detection is readily achievable with VUV. Sam also highlighted the use of MOCCA2, an open-source Python package for automated chromatographic data processing, ideal for baseline correction, peak purity assessment, deconvolution, and compound tracking:

https://bayer-group.github.io/MOCCA/

3 – GC×GC–QTOF: Managing the Data Explosion

(Paul O’Nion, RSSL, Reading)

Complex samples, environmental matrices, petrochemicals, foods, fragrances, all may contain hundreds to thousands of organic compounds, often beyond the resolving power of conventional GC–MS. Paul presented the capabilities of GC×GC–QTOF (two-dimensional GC coupled to quadrupole time-of-flight MS), a technique offering dramatically enhanced separation and structural elucidation.

Using two orthogonal GC columns (typically non-polar × polar) and a modulator, GC×GC spreads compounds across a 2D retention space, revealing components that would otherwise co-elute in 1D GC. However, distinguishing isomers can remain challenging. Switching from hard EI (70 eV) to soft ionisation (approx. 10 eV) significantly reduces fragmentation, making the molecular ion dominant and enabling clearer structural interpretation.

Paul also noted the practical realities: GC×GC datasets often reach 20–30 GB per run, requiring advanced data-management strategies. He concluded with a detector comparison across FID, VUV, and MS(SIM):

	FID	VUV	MS (SIM) Single Ion Monitoring
Cost	3 (cheapest)	2 (mid-range)	1 (most expensive)
Ease of Use	3 (plug-and-play, minimal training)	3 (moderate, needs spectra processing)	1 (calibration, tuning, vacuum)
Sensitivity	1 (ppm, analyte-dependent)	2 (ppm, some sub ppm analyte-dependent)	3 (ppb-sub-ppb, ultra-trace)
Selectivity	1 (none)	2 (spectral fingerprints, deconvolution)	3 (m/z selective, structural info)
Linearity / Range	3 (>106 dynamic range)	2 (~103, sometimes 104)	2 (103 – 104)
Calibration burden	2 (variable response factors)	2 (class-based responses simplify)	2 (isotopic standards, complex)
Routine robustness	3 (very robust)	3 (good, requires lamp checks)	1 (more maintenance)
Best use case	QC	Precursor screening	Ultra-trace nitrosamines

4 – GC–MS Steroid Profiling: Past Lessons, Present Practice, and Future Horizons

(Angela Taylor, University of Birmingham)

Angela Taylor leads the Steroid Metabolome Analysis Core (SMAC), a research group specialising in developing and validating mass spectrometry methods for hormonal and metabolic studies in both clinical and laboratory settings. Her work spans cancer, infertility, stress, trauma, and broader metabolic disorders.

The talk she focused on steroids profiling. An interesting point that she mentioned about hormones is that they follow diurnal rhythm, and the hormone concentration in the blood changes over the course of the day. For example, cortisol increases overnight and peaks first thing in the morning. There is the ability to collect cortisol from dried urine testing however is that urinary cortisol reflects an average of the time since the previous urine void, while saliva provides an instantaneous assessment at the time saliva was collected. Therefore, the first morning urine collection is representative of overnight cortisol production.

Figure 1 - Courtesy of ZRTlab

Angela contrasted the strengths of GC–MS versus LC–MS/MS for steroid analysis:

	GC-MS	LC-MS/MS
Detection of	Free steroids (deconjugated steroids)	Steroid conjugates Free steroids
Use	Urinary steroid profile Specific serum metabolite	Specific urine metabolite Specific serum metabolite
Sample Prep time	Derivatisation (3 days) 20-30 samples / week	0.5 days 100 samples / day
Analysis time	>30 mins	~10 min
Cost	£150 / sample £60 k / instrument	£70 / sample £300 k / instrument

Due to steroids having different alkane chain lengths, making GC rulers for this is important. As chain length increases, the boiling point, melting point and density of the alkane generally increases due to greater van der Waals forces between the molecules. Kovats retention index is used in gas chromatography and is used to convert retention times into system independent constants. Retention times will vary due to column length, film thickness, diameter and inlet pressure, the derived retention indices are quite independent of these parameters and allow comparing values measured by different analytical laboratories under varying conditions and analysis times from seconds to hours.

5 – Increasing Productivity Sustainably with the Agilent 8850

(Bryan White, Agilent, Cheadle)

This session introduced the Agilent 8850 GC, focusing on engineering improvements rather than analytical theory. Key features included:

• Reduced instrument footprint

• More precise air-bath oven control

• Up to 45% lower power consumption

• Advanced monitoring for system health and uptime

The broader message aligned with industry trends toward energy-efficient, compact, and operator-friendly GC platforms.

6 – Advances in Automation

(Marco Wolff, Gerstel, Germany)

Marco Wolff showcased automation solutions from Gerstel, particularly highlighting the TVOC Wizard, a software tool that streamlines the workflow for analysing Total Volatile Organic Compounds. It simplifies data handling, integration, and reporting, reducing manual steps and improving reproducibility.

TVOC Wizard: https://www.gerstel.com/en/products/Software-Data-Evaluation/TVOC-Wizzard

This talk highlighted how software driven automation remains central to achieving consistency in high-throughput GC laboratories.

7 – New Dimensions in Non-Targeted Screening of Complex Samples

(Geraint Morgan, University of Southampton)

Non-targeted screening is essential for evaluating food contact materials (FCMs)—plastics, papers, films, and composites used in packaging. These materials may leach chemical constituents into food during processing, transport, or storage, posing potential health risks.

Geraint outlined the analytical and computational challenges posed by the vast chemical diversity in FCMs, where migration behaviour can be unpredictable. Advanced chromatographic strategies and software-assisted identification tools are essential for identifying unknowns and evaluating risk.

Figure credit: doi:10.3390/foods12224135

8 – The Use of Statistics in GC: From Design of Experiments to Data Analysis

(Kathy Ridgway, Da Vinci Laboratory Solutions)

Kathy provided a concise overview of Design of Experiments (DoE) and chemometrics, emphasising how statistical approaches can dramatically improve method development and data interpretation.

Chemometrics applies mathematical and statistical models to chemical data, enabling better pattern recognition, optimisation, and predictive modelling. She recommended the textbook Statistics and Chemometrics by the Miller family:

Download link: https://www.africanfoodsafetynetwork.org/wp-content/uploads/2021/09/Statistics-and-Chemometrics-2010.pdf

Da Vinci Laboratory Solutions partners with analytical labs to deliver customised chromatography solutions across industries.

9 – Gas Analysis and Reactor Monitoring: Old Challenges and New Technology

(Andrew Cissold, Shimadzu, Milton Keynes)

Andrew discussed advances in industrial gas analysis. Industrial processes increasingly involve the use and manufacture of highly dangerous substances, particularly toxic and combustible gases. Identifying dangers early and accurately is of paramount importance, because gas hazards can be invisible and hard to identify. Detectors like FID (Flame ionisation detector) and TCD (Thermal Conducting Detector).

TCD works by measuring the difference in thermal conductivity between a pure carrier gas and the gas mixture containing the sample. Since most compounds have a thermal conductivity much less than that of the common carrier gases of helium or hydrogen, when an analyte elutes from the column the effluent thermal conductivity is reduced and a detectable signal is produced.

The TCD is a good general purpose detector for initial investigations with an unknown sample compared to the FID that will react only to combustible compounds (Ex: hydrocarbons). Moreover, the TCD is a non-specific and non-destructive technique. The TCD is also used in the analysis of permanent gases (argon, oxygen, nitrogen, carbon dioxide) because it responds to all these substances unlike the FID which cannot detect compounds which do not contain carbon-hydrogen bonds.

10 – Sustainable GC Innovation: Toward Unified Plastics Analysis

(Ruth Godfrey, Swansea University)

Ruth Godfrey presented an engaging talk connecting exposomics, sustainability, and analytical innovation. Monitoring human exposure to plastics—particularly phthalates and microplastics, is becoming increasingly important for public health.

A key analytical technique is pyrolysis GC–MS, which thermally breaks down polymers and allows quantification of materials such as polyethylene (PE), polypropylene (PP), and polystyrene (PS), even at low concentrations. Their small size and poor solubility make microplastics analytically challenging, so pyrolysis remains the most robust approach.

Agilent provides an excellent overview of this method in their application note:

https://www.agilent.com/cs/library/applications/application-microplastics-gc-msd-5994-2199en-agilent.pdf

Ruth’s talk demonstrated how sustainable GC methods—and unified workflows for plastics analysis—are increasingly essential in environmental health research.