Challenges in data processing and evaluation of scent samples analysed by GCxGC-TOF

Significance of the Topic

This study addresses critical challenges in processing comprehensive two-dimensional gas chromatography–mass spectrometry (GC×GC-MS) data of human scent samples. Accurate data alignment and peak identification are essential for applications in forensic analysis, quality control in fragrance and odor research, and biomarker discovery.

Objectives and Study Overview

The primary aim was to compare and evaluate data-processing approaches for complex scent chromatograms and to highlight limitations of ChromaTOF software versions 5.51.06.0 and 5.55.41_BT. The work builds on prior methodology for human scent sampling and seeks to identify a robust strategy for reliable peak detection and repeatability assessment.

Methodology and Instrumentation

Sample Collection and Preparation:

• Volatile compounds were collected from volunteer palms using pre-cleaned 3.6 mm glass beads.
• Each bead was extracted twice with 1 mL ethanol; extracts were combined, evaporated, and re-dissolved in 70 µL ethanol.

Chromatographic Analysis:

• Instrument: GC×GC-MS system (BT-4D, Leco).
• First column: mid-polar Rtx-200MS; oven program from 40 °C (2 min) to 320 °C at 5 °C/min, hold 10 min.
• Second column: non-polar TG-5HT; temperature offset by +5 °C relative to the first column.
• Modulation periods: 6 s, 8 s, and 10 s.

Data-Processing Approaches:

1. DA_2DCHROM automatic alignment – anchor-point algorithm; failed on dense human scent profiles.
2. Kovats retention index method – 15 reference peaks per chromatogram; semi-automatic but subjective and not consistently reliable.
3. Target-peak selection – manual annotation of up to 300 peaks; accurate yet extremely time-consuming and operator-dependent.

Main Results and Discussion

Analysis revealed that neither automatic alignment nor index-based approaches provided satisfactory repeatability for BT-4D data. Manual target-peak selection yielded the highest success rate but posed significant workload. Key software limitations in ChromaTOF versions 5.51.06.0/5.55.41_BT include:

• Omission of sub-peaks after deconvolution.
• Inconsistent base-peak selection across runs.
• Undefined integration boundaries leading to quantification errors.

Earlier ChromaTOF version 4.72.0.0 allowed user control over minimum peak width, maximum retention shift in the second dimension, and signal-to-noise thresholds, features removed in later versions, exacerbating data-processing errors.

Benefits and Practical Applications

The findings guide analysts toward best practices in GC×GC-MS scent profiling, emphasizing the need for manual validation in high-complexity samples. Improved data-processing workflows enhance the reliability of volatile compound identification, benefiting forensic odour matching, fragrance quality control, and clinical volatile biomarker studies.

Future Trends and Potential Applications

Advancements likely include integration of machine-learning algorithms for automated peak alignment and deconvolution, development of open-source tools with configurable parameters, and expansion of workflows to other complex biological or environmental sample matrices. Collaborative standardization of retention indices and spectral libraries will further improve comparability across laboratories.

Conclusion

Current automated and semi-automated data-processing approaches for GC×GC-MS human scent analysis are insufficient for high-density chromatograms. Manual target-peak selection remains the most reliable, albeit laborious. Upgraded software flexibility and novel computational strategies are required to achieve robust, reproducible results.

Reference

• Pojmanová P, Ladislavová N, Škeříková V, Kania P, Urban Š. Chemical Papers. 2020;74:1383–1393.
• Pojmanová P, Ladislavová N, Urban Š. Development of a Method for the Measurement of Human Scent Samples Using Comprehensive Two-Dimensional Gas Chromatography with Mass Detection. Separations. 2021;8:232.
• Ladislavová N, Pojmanová P, Urban Š. DA_2DCHROM—a data alignment tool for applications on real GC×GC–TOF samples. Anal Bioanal Chem. 2023;415:2641–2651.