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Surrogate-matrix calibration for quantitative GC-IMS headspace analysis of urine
Gema Guedes de la Cruz1, Tecla Duran-Fort1, Luis Fernandez2
1Institute for Bioengineering of Catalonia (IBEC), The Barcelona Institute of Science and Technology, Barcelona, 08028, Spain.
Accurate quantification of volatile organic compounds (VOCs) in urine using static headspace gas chromatography-ion mobility spectrometry (HS-GC-IMS) is now possible. A novel matrix adaptation method improves accuracy by adjusting for partitioning and detector effects, enabling reliable calibration in complex biological samples.
Area of Science:
- Analytical Chemistry
- Biomarker Discovery
- Spectrometry
Background:
- Quantitative analysis of volatile organic compounds (VOCs) in biological samples like urine is complex.
- Challenges include matrix effects on headspace partitioning, endogenous compounds, and non-linear detector responses.
- Static headspace gas chromatography-ion mobility spectrometry (HS-GC-IMS) is sensitive but requires robust calibration methods.
Purpose of the Study:
- To develop a practical framework for accurate quantitative VOC analysis in urine using HS-GC-IMS.
- To address challenges of matrix-dependent partitioning and non-linear detector responses.
- To enable reliable calibration in complex biological matrices without individual recalibration.
Main Methods:
- Developed a quantitative framework for HS-GC-IMS analysis of VOCs in urine.
- Utilized a synthetic urine surrogate matrix for initial calibration.
- Employed an affine adjustment of the concentration axis to adapt calibration to real urine samples, accounting for matrix effects and non-linear detector behavior.
- Evaluated the method using three colorectal cancer-related VOCs (anisole, 2-heptanone, 2-pentanone) in the 0-30 ppb range.
Main Results:
- The matrix adaptation framework significantly improved quantitative accuracy for VOCs in real urine samples.
- The method successfully accounted for matrix-dependent partitioning and non-linear detector responses.
- Quantitative accuracy was enhanced, especially for compounds with strong matrix-dependent partitioning.
- Limitations were identified at high endogenous concentrations near detector saturation.
Conclusions:
- The proposed strategy enables reliable surrogate-matrix calibration for quantitative VOC analysis in complex biological samples like urine.
- The method is adaptable to other headspace-based analytical platforms facing similar matrix effects.
- This approach facilitates more accurate biomarker discovery and monitoring using VOC profiling.
