identification of chemical drivers of coffee bitterness ... · bioresponse-guided decomposition of...
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Identification of Chemical Drivers of Coffee Bitterness Using FlavoromicsChengyu Gao and Devin Peterson*
*Corresponding author, [email protected] of Food Science and Technology, The Ohio State University, USA
Flavoromics Workflow
Coffee is one of the most popular beverages in the world with an estimated market of200 billion. As consumers are willing to pay more for specialty coffee, it is important tounderstand coffee flavor complexity to meet consumer demands. Thus, understanding themolecular basis of coffee bitterness is of high interest. A few bitter compounds in coffee havebeen identified; however, the sensory relevance of these compounds and their contribution tooverall coffee bitterness are not well established.[1,2]The taste-guided method, which is widelyused in literature, only focus on direct actives but is not applicable to identify flavormodulators that are not taste active.
The objective of this project was to apply an untargeted flavoromics approach toidentify chemical drivers of bitterness in roasted coffee brew samples and enable thediscovery of flavor modulator compounds.
Untargeted chemical profiling by UPLC/MS-(QToF) and sensory evaluation of 14coffee brews samples were carried out. Multivariate statistical analyses orthogonal projectionto latent structures (OPLS) was subsequently employed to select chemical markers highlypredictive of coffee bitterness. Isolation, identification, and sensory validation of selectedchemical markers are ongoing. This project will provide a new understanding of chemistrydriving overall coffee bitterness and allow the industry to tailor coffee products to differentconsumer preferences.
2. Multivariate statistical analysis and marker selection
• Untargeted flavoromics analysis was successfully applied to investigate markers that highly correlated to bitterness in coffee brew. Selected negative markers have been isolated and purified with more than 90% purity.
• Sensory recombination test of negative markers showed significant modulation effect on coffee bitterness. Sensory evaluation of individual negative markers is ongoing.
• Three negative markers have been identified as caffeoylquinic acid derivatives.• Future work will focus on isolation and identification of positive markers. Sensory recombination
of positive markers will be carried out to investigate their sensory relevance.
14 commercial beans (7 manufacturers)
Sensory evaluation Chemical profiling Data processing
Multivariate statistical analysis
Isolate and purify markers
Sensory recombination
1. Bitterness evaluation of roasted coffee brew
OPLS - coffee brew modeled with bitter intensity
S-plot of coffee brew with marker selection
Compound identification
Negative Markers VIP scoreMarker 8 10.7Marker 9 10.7
Marker 10 9.0Marker 11 8.0Marker 12 4.2Marker 13 4.0
6. Identification of negative markers
Comparison of RT (A) and MS/MS fragmentation of 4,5-diCQA standard (B) and Marker 13 (C)
UPLC-Mass spectrometry• MS/MS experiments were performed in
negative and positive ESI to obtaininformation on fragmentation patterns andelemental composition of the marker
• Three negative markers were identified ascaffeoyl quinic acid derivatives
Nuclear magnetic resonance (NMR)• Mono and bidimensional NMR will be
performed to obtain accurate structuralinformation of unknown compounds
Conclusion and Future Work
References
A
B
C
1. Frank, O., Zehentbauer, G., & Hofmann, T. (2006). Bioresponse-guided decomposition of roast coffee beverage and identification of key bitter taste compounds. European Food Research and Technology, 222(5-6), 492-508. doi:10.1007/s00217-005-0143-6
2. Kreppenhofer, S.; Frank, O.; Hofmann, T. Identification of (furan-2-yl)methylated benzene diols and triols as a novel class of bitter compounds in roasted coffee. Food Chem. 2011, 126, 441−449.
Abstract
1. Sensory evaluation of coffee brews• Caffeine solution as standards• 0-15 intensity scale • 8 trained panelists, 2 replicates
Materials and Methods
Ground coffee/water1:20 ratio
Sample clean upSPE 96 wellplates
C18 columnUPLC/MS(QToF) ESI- mode
14 commercial roasted beans
2. Chemical profiling of roasted coffee brew
3. Data processing and multivariate statistical analysis
Data transform Data filtration Multivariate analysis
Remove noise and inconsistent data• Signal intensity < 750 ion count• CV > 20% in QC samples
Transform chromatographic data into features
(retention time, m/z)PCA, OPLS, S-plot
Results and Discussion
Positive Markers VIP scoreMarker 1 5.0Marker 2 4.6Marker 3 4.5Marker 4 4.3Marker 5 3.7Marker 6 3.5Marker 7 3.4
3. Isolation and purification of markers
1st dimension fractionation(Extract from coffee beans)
2nd D fractionation>50% purity
3rd D fractionation> 90% purity
TIC of coffee brew extract1st dimension fractionation
TIC of purified marker(3rd D fractionation)
PCA – chemical variation of coffee brew
4. Quantification of negative markers in coffee brew
5. Sensory recombination test
Control (high bitter coffee)
negative markers(pH reduced 0.37)
adjusted pH
10.33 9.928.78
0
2
4
6
8
10
12
Control Control adjusted pH Control + negative markers
Biite
r Int
ensi
ty
*
• Anova, post-hoc Turkey, *p = 0.001
194.7 192.0
13.3 17.3
42.7
140.9
18.3
43.0
73.3
25.817.4 11.2 7.0 10.4 13.3
44.0
4.9 9.0
28.511.2
0
40
80
120
160
200
1 2 3 4 5 6 7 8 9 10
Conc
entr
atio
n (m
g/L
in d
rip c
offe
e)
Negative markers
Low Bitter Coffee
High Bitter Coffee
TIC of 4,5-diCQA STDTIC of Marker 13
MSMS of 4,5-diCQA at 20 V
MSMS of Marker 13 at 20 V