Profiling analysis of volatile compounds from fruits using comprehensive two-dimensional gas chromatography and image...

Area of Science:

• Analytical Chemistry
• Chemometrics
• Food Science

Background:

• Comprehensive two-dimensional gas chromatography (GCxGC) generates complex datasets for volatile compound analysis.
• Traditional data processing methods can be labor-intensive and prone to variations.
• Image processing techniques from proteomics offer potential for advanced data analysis.

Purpose of the Study:

• To adapt and apply an image processing workflow from proteomics for GCxGC data analysis.
• To demonstrate the utility of this approach for unbiased volatile profiling and pattern comparison.
• To differentiate volatile profiles of various fruits, including apples, pears, and quince.

Main Methods:

• Headspace solid-phase microextraction (HS-SPME) coupled with GCxGC was used to obtain volatile profiles.
• GCxGC chromatograms were converted to gray-scale images and analyzed using a proteomics-derived workflow.
• Image warping corrected run-to-run variations, and fusion images created a consensus spot pattern for quantification and statistical analysis.

Main Results:

• The image processing approach successfully compensated for GCxGC run-to-run variations.
• A consensus pattern enabled the calculation of over 700 gap-free volatile profiles across all samples.
• Multivariate statistical analysis of these profiles allowed for effective sample clustering and prediction of unknown samples.

Conclusions:

• The adapted image processing workflow provides a powerful tool for unbiased analysis of GCxGC data.
• This method facilitates robust volatile profiling and differentiation of complex sample matrices like fruit aromas.
• Future work can integrate mass spectrometry data for compound identification and targeted analysis.