HPLC-MS-based design of experiments approach on cocoa roasting
Paweł J Andruszkiewicz1, Marcello Corno2, Nikolai Kuhnert1
1Department of Life Sciences and Chemistry, Jacobs University Bremen, Campus Ring 1, 28759 Bremen, Germany.
Food Chemistry
|May 14, 2021
Summary
This study models cocoa roasting using statistical methods to understand how process changes affect flavor compounds. The findings help optimize roasting for better chocolate taste.
Area of Science:
- Food Science
- Chemical Engineering
- Statistical Modeling
Background:
- Statistical methods like design of experiments are crucial for food processing but underutilized in cocoa roasting.
- Understanding cocoa roasting's impact on flavor compounds is key to chocolate quality.
Purpose of the Study:
- To develop a predictive model for cocoa roasting using design of experiments.
- To link key processing parameters to changes in flavor-related chemical constituents.
Main Methods:
- Employed design of experiments to systematically vary roasting parameters.
- Utilized High-Performance Liquid Chromatography-Mass Spectrometry (HPLC-MS) to quantify chemical compounds.
- Analyzed the influence of time, temperature, and additives (water, acid, base) on specific compounds.
Main Results:
- Established high-quality, validated models for the relative concentrations of procyanidins, Amadori compounds, and peptides.
- Demonstrated the significant impact of roasting parameters on these flavor precursors.
- Achieved good prediction accuracy for the established models.
Conclusions:
- The developed models accurately predict chemical changes during cocoa roasting.
- This approach enables optimization of cocoa roasting conditions to control flavor compound concentrations.
- Potential to enhance chocolate flavor profiles through precise process control.
