Uncovering the Volatile Profile of Potato Taste Defect in Roasted Arabica Coffee using GC-MS, GC×GC-MS, and Chemometrics

Significance of the Topic

Coffee aroma and flavor quality are critical for consumer acceptance and market value. Potato Taste Defect (PTD) produces an unpleasant “dirty potato” note in green and roasted Arabica beans from the African Great Lakes region. Profiling the volatile changes associated with PTD enables early detection, quality control, and mitigation strategies in coffee production.

Aim and Study Overview

This study aims to comprehensively characterize the volatile profile of PTD-affected roasted Arabica coffee by combining targeted and non-targeted analyses. Researchers compared clean, mild, medium, and strong PTD samples to identify discriminatory compounds and relate them to the key marker 2-isopropyl-3-methoxypyrazine (IPMP).

Methodology and Instrumentation

A four-step workflow was employed:

• Classification of coffee samples by PTD intensity (clean, mild, medium, strong).
• GC-MS targeted quantification of IPMP to confirm its variation among defect levels.
• GC×GC-TOFMS for comprehensive volatile profiling, detecting around 500 peaks per sample.
• Tile-based Fisher ratio analysis to extract statistically significant features, followed by PLS regression to model IPMP concentrations.

Used Instrumentation

• Gas Chromatography–Mass Spectrometry (GC-MS) for targeted IPMP measurements.
• Comprehensive Two-Dimensional GC with Time-of-Flight MS (GC×GC-TOFMS) for enhanced peak capacity and separation.
• Chemometric tools for F-ratio analysis and PLS regression.

Key Results and Discussion

• IPMP levels varied significantly with PTD severity (p<0.05).
• GC×GC-TOFMS revealed reduced overall signal in strong PTD samples.
• Tile-based F-ratio identified 359 discriminating volatiles (p<0.01): 327 downregulated in PTD samples (linked to desirable nutty, fruity, cocoa notes) and 32 upregulated in defective beans (associated with insect or microbial metabolites).
• PLS regression accurately predicted IPMP concentration (NRMSECV 10.6%, NRMSEP 9.9%), confirming the relevance of non-targeted features.
• Regression vectors distinguished analytes positively and negatively correlated with IPMP, highlighting altered aroma pathways.

Benefits and Practical Applications

• The combined analytical–chemometric approach enables robust detection of PTD markers for QA/QC.
• Early identification of defective batches supports remediation in processing and roasting.
• Framework adaptable to other coffee quality issues and volatile-based defect monitoring.

Future Trends and Opportunities

• Deployment of portable GC×GC-MS systems for in-field coffee assessment.
• Machine learning on large-scale volatile datasets for predictive quality modeling.
• Elucidation of biological origins of PTD volatiles to guide agricultural interventions.
• Application of similar workflows to other coffee defects and flavor profiling studies.

Conclusion

This work demonstrates that PTD induces substantial shifts in the volatile landscape of roasted Arabica coffee, diminishing desirable aroma compounds while elevating off-flavor markers. The integration of GC×GC-TOFMS with chemometric analyses provides a powerful platform for comprehensive defect profiling, facilitating improved quality control and defect mitigation strategies.

Reference

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