Omics approaches to understand cocoa processing and chocolate flavor development: A review

被引:17
作者
Herrera-Rocha, Fabio [1 ]
Fernandez-Nino, Miguel [2 ]
Cala, Monica P. [3 ]
Duitama, Jorge [4 ]
Barrios, Andres Fernando Gonzalez [1 ]
机构
[1] Univ Los Andes, Dept Chem & Food Engn, Grp Diseno Prod & Proc GDPP, Bogota 111711, Colombia
[2] Leibniz Inst Plant Biochem, Dept Bioorgan Chem, Weinberg 3, D-06120 Halle, Germany
[3] Univ Los Andes, MetCore Metabol Core Facil, Vice Presidency Res, Bogota, Colombia
[4] Univ Los Andes, Syst & Comp Engn Dept, Bogota 111711, Colombia
关键词
Cocoa; Food processing; Data mining; Food omics; THEOBROMA-CACAO L; GEOGRAPHICAL ORIGIN; VOLATILE COMPOUNDS; BEAN FERMENTATION; AROMA; PROFILE; IMPACT; QUANTIFICATION; OLIGOPEPTIDES; COMMUNITIES;
D O I
10.1016/j.foodres.2023.112555
中图分类号
TS2 [食品工业];
学科分类号
0832 ;
摘要
The global market of chocolate has increased worldwide during the last decade and is expected to reach a value of USD 200 billion by 2028. Chocolate is obtained from different varieties of Theobroma cacao L, a plant domesticated more than 4000 years ago in the Amazon rainforest. However, chocolate production is a complex process requiring extensive post-harvesting, mainly involving cocoa bean fermentation, drying, and roasting. These steps have a critical impact on chocolate quality. Standardizing and better understanding cocoa processing is, therefore, a current challenge to boost the global production of high-quality cocoa worldwide. This knowledge can also help cocoa producers improve cocoa processing management and obtain a better chocolate. Several recent studies have been conducted to dissect cocoa processing via omics analysis. A vast amount of data has been produced regarding omics studies of cocoa processing performed worldwide. This review systematically analyzes the current data on cocoa omics using data mining techniques and discusses opportunities and gaps for cocoa processing standardization from this data. First, we observed a recurrent report in metagenomics studies of species of the fungi genus Candida and Pichia as well as bacteria from the genus Lactobacillus, Acetobacter, and Bacillus. Second, our analyzes of the available metabolomics data showed clear differences in the identified metabolites in cocoa and chocolate from different geographical origin, cocoa type, and processing stage. Finally, our analysis of peptidomics data revealed characteristic patterns in the gathered data including higher diversity and lower size distribution of peptides in fine-flavor cocoa. In addition, we discuss the current challenges in cocoa omics research. More research is still required to fill gaps in central matter in chocolate production as starter cultures for cocoa fermentation, flavor evolution of cocoa, and the role of peptides in the development of specific flavor notes. We also offer the most comprehensive collection of multi-omics data in cocoa processing gathered from different research articles.
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页数:13
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共 101 条
[41]  
ICCO, 2019, FIN FLAV COC
[42]   Applying meta-pathway analyses through metagenomics to identify the functional properties of the major bacterial communities of a single spontaneous cocoa bean fermentation process sample [J].
Illeghems, Koen ;
Weckx, Stefan ;
De Vuyst, Luc .
FOOD MICROBIOLOGY, 2015, 50 :54-63
[43]   Phylogenetic Analysis of a Spontaneous Cocoa Bean Fermentation Metagenome Reveals New Insights into Its Bacterial and Fungal Community Diversity [J].
Illeghems, Koen ;
De Vuyst, Luc ;
Papalexandratou, Zoi ;
Weckx, Stefan .
PLOS ONE, 2012, 7 (05)
[44]   Theobroma cacao L. cultivar CCN 51: a comprehensive review on origin, genetics, sensory properties, production dynamics, and physiological aspects [J].
Jaimez, Ramon E. ;
Barragan, Luigy ;
Fernandez-Nino, Miguel ;
Wessjohann, Ludger A. ;
Cedeno-Garcia, George ;
Cantos, Ignacio Sotomayor ;
Arteaga, Francisco .
PEERJ, 2022, 10
[45]   Nanopore sequencing and assembly of a human genome with ultra-long reads [J].
Jain, Miten ;
Koren, Sergey ;
Miga, Karen H. ;
Quick, Josh ;
Rand, Arthur C. ;
Sasani, Thomas A. ;
Tyson, John R. ;
Beggs, Andrew D. ;
Dilthey, Alexander T. ;
Fiddes, Ian T. ;
Malla, Sunir ;
Marriott, Hannah ;
Nieto, Tom ;
O'Grady, Justin ;
Olsen, Hugh E. ;
Pedersen, Brent S. ;
Rhie, Arang ;
Richardson, Hollian ;
Quinlan, Aaron R. ;
Snutch, Terrance P. ;
Tee, Louise ;
Paten, Benedict ;
Phillippy, Adam M. ;
Simpson, Jared T. ;
Loman, Nicholas J. ;
Loose, Matthew .
NATURE BIOTECHNOLOGY, 2018, 36 (04) :338-+
[46]   EFFECT OF DRYING ON ACIDITY AND VOLATILE FATTY-ACIDS CONTENT OF COCOA BEANS [J].
JINAP, S ;
THIEN, J ;
YAP, TN .
JOURNAL OF THE SCIENCE OF FOOD AND AGRICULTURE, 1994, 65 (01) :67-75
[47]   Experimentally modelling cocoa bean fermentation reveals key factors and their influences [J].
John, Warren A. ;
Boettcher, Nina L. ;
Behrends, Britta ;
Corno, Marcello ;
D'souza, Roy N. ;
Kuhnert, Nikolai ;
Ullrich, Matthias S. .
FOOD CHEMISTRY, 2020, 302
[48]   Forcing fermentation: Profiling proteins, peptides and polyphenols in lab-scale cocoa bean fermentation [J].
John, Warren A. ;
Bottcher, Nina L. ;
Asskamp, Maximilian ;
Bergounhou, Audrey ;
Kumari, Neha ;
Ho, Ping-Wei ;
D'Souza, Roy N. ;
Nevoigt, Elke ;
Ullrich, Matthias S. .
FOOD CHEMISTRY, 2019, 278 :786-794
[49]   Aseptic artificial fermentation of cocoa beans can be fashioned to replicate the peptide profile of commercial cocoa bean fermentations [J].
John, Warren A. ;
Kumari, Neha ;
Boettcher, Nina L. ;
Koffi, Kouame Jean ;
Grimbs, Sergio ;
Vrancken, Gino ;
D'Souza, Roy N. ;
Kuhnert, Nikolai ;
Ullrich, Matthias S. .
FOOD RESEARCH INTERNATIONAL, 2016, 89 :764-772
[50]   High-accuracy long-read amplicon sequences using unique molecular identifiers with Nanopore or PacBio sequencing [J].
Karst, Soren M. ;
Ziels, Ryan M. ;
Kirkegaard, Rasmus H. ;
Sorensen, Emil A. ;
McDonald, Daniel ;
Zhu, Qiyun ;
Knight, Rob ;
Albertsen, Mads .
NATURE METHODS, 2021, 18 (02) :165-+