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Profiling Volatile Compounds in Blackcurrant Fruit using Headspace Solid-Phase Microextraction Coupled to Gas Chromatography-Mass Spectrometry

Profiling Volatile Compounds in Blackcurrant Fruit using Headspace Solid-Phase Microextraction Coupled to Gas Chromatography-Mass Spectrometry

Published on: June 9, 2021

Headspace SPME GC-MS Analysis of Urinary Volatile Organic Compounds (VOCs) for Classification Under Sample-Limited

Lea Woyciechowski¹, Tushar H More¹, Sabine Kaltenhäuser¹

¹Department of Bioinformatics and Biochemistry, BRICS-Braunschweig Integrated Centre of Systems Biology, Technische Universität Braunschweig, 38106 Braunschweig, Germany.

|January 27, 2026

Abstract

Background/Objectives:

Volatile organic compounds (VOCs) are emerging as non-invasive biomarkers of metabolic and disease-related processes, yet their reliable detection from complex biological matrices such as urine remains analytically challenging. This study aimed to establish a robust, non-targeted headspace solid-phase microextraction gas chromatography-mass spectrometry (HS-SPME GC-MS) workflow optimized for very small-volume urinary samples.

Methods:

We systematically evaluated the effects of pH adjustment and NaCl addition on VOC extraction efficiency using a 75 µm CAR/PDMS fiber and a sample volume of only 0.75 mL. Method performance was further assessed using concentration-dependent experiments with representative VOC standards and by application to real human urine samples analyzed in technical triplicates.

Results:

Acidification to pH 3 markedly improved extraction performance, increasing both total signal intensity and the number of detectable VOCs, whereas alkaline conditions and additional NaCl produced only minor effects. Representative VOC standards showed compound-specific linear dynamic ranges with minimal carry-over within the relevant analytical range. Application to real urine samples confirmed high analytical reproducibility, with triplicates clustering tightly in principal component analysis and most metabolites exhibiting relative standard deviations below 25%.

Conclusions:

The optimized HS-SPME GC-MS method enables comprehensive, non-targeted urinary VOC profiling from limited sample volumes. This workflow provides a robust analytical foundation for exploratory volatilomics studies under sample-limited conditions and supports subsequent targeted method refinement once specific compounds or chemical classes have been prioritized.

Keywords:

CAR/PDMS fiber GC–MS HS–SPME classification non-targeted profiling pH adjustment small sample volume urinary VOCs urine volatilomics

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