A non-targeted data processing workflow for volatile organic compound data acquired using comprehensive...

Area of Science:

• Analytical Chemistry
• Chromatography

Background:

• Recent technological advancements in comprehensive two-dimensional gas chromatography (2D-GC) necessitate standardized data processing workflows.
• Analyzing complex samples like kava (Piper methysticum) with rich volatile organic compound (VOC) profiles benefits from multidimensional approaches offering enhanced peak capacity.

Purpose of the Study:

• To develop and validate an integrated batch data processing workflow for dual-channel detection systems (flame ionization detection and quadrupole mass spectrometry).
• To automate compound identification and quantification in VOC analysis using 2D-GC.

Main Methods:

• Customized a data processing workflow from manual single-sample analysis to an integrated batch workflow.
• Defined parameter choices for baseline correction and peak detection suitable for batch data handling.
• Utilized quadrupole mass spectrometry (qMS) data to define elution regions for automated compound identification.
• Transformed stencils onto flame ionization detection (FID) data for quantitative analysis.

Main Results:

• Successfully transitioned from manual to an automated, reproducible batch workflow for 2D-GC-FID/qMS data.
• Enabled automated compound identification by defining elution regions using qMS data.
• Facilitated quantitative analysis by applying identified compound information (stencils) to FID data.

Conclusions:

• The developed workflow enhances reproducibility in multidimensional chromatography data processing.
• The automated workflow facilitates the analysis of complex VOC mixtures, such as those found in kava.
• The provided dataset and detailed methods serve as a training tool for wider adoption in new application areas.