Cocoa smoky off-flavour: A MS-based analytical decision maker for routine controls.

Area of Science:

• Food Science
• Analytical Chemistry
• Sensory Science

Background:

• Smoky off-flavors in cocoa beans result from artificial drying, negatively impacting chocolate quality.
• Current sensory tests are time-consuming, and rapid analytical methods for detecting this defect are lacking.

Purpose of the Study:

• To develop and validate a fast, objective analytical method for detecting smoky off-flavors in cocoa beans.
• To utilize headspace-solid phase microextraction-mass spectrometry-electronic nose (HS-SPME-MS-enose) coupled with chemometrics for defect detection.

Main Methods:

• Application of HS-SPME-MS-enose combined with chemometric models (SIMCA and PLS-DA).
• Analysis of diagnostic mass-spectral patterns to differentiate between normal and smoked cocoa samples (beans and liquors).
• Confirmation of discrimination ability using quantitative HS-SPME-GC-MS analysis.

Main Results:

• SIMCA models achieved high classification performance, with sensitivities over 90% and specificities ranging from 89-100%.
• Diagnostic ions were identified and linked to phenolic derivatives, indicating the source of the off-flavor.
• The method demonstrated effective discrimination between defective and non-defective cocoa samples.

Conclusions:

• HS-SPME-MS-enose coupled with chemometrics is a rapid, cost-effective, and objective approach for high-throughput screening of defective cocoa samples.
• This analytical strategy can effectively identify cocoa beans affected by smoky off-flavors, aiding in quality control.
• The developed method addresses the need for a fast analytical solution to complement traditional sensory evaluations in the cocoa industry.