Investigation of aromatic compounds and olfactory profiles in cocoa pulp fermentation using yeast-based starters: A Volatilomics and machine learning approach
文献类型： 外文期刊
作者： Chang, Haode 1 ; Gu, Chunhe 2 ; Zhang, Quanmiao 1 ; Zhang, Wenjing 1 ; Ma, Liru 2 ; Liu, Fei 1 ; Feng, Zhen 2 ;
作者机构： 1.Northeast Agr Univ, Coll Food Sci, Key Lab Dairy Sci, Minist Educ, Harbin 150030, Peoples R China
2.Chinese Acad Trop Agr Sci, Spice & Beverage Res Inst, Wanning 571533, Hainan, Peoples R China
3.Key Lab Proc Suitabil & Qual Control Special Trop, Wanning 571533, Hainan, Peoples R China
4.Northeast Forestry Univ, Coll Life Sci, Harbin 150006, Peoples R China
关键词： Cocoa; Fermentation; Machine learning; Aroma; GC-MS
期刊名称：FOOD CHEMISTRY-X （ 影响因子：8.2； 五年影响因子：8.2 ）
ISSN： 2590-1575
年卷期： 2025 年 26 卷
页码：
收录情况： SCI
摘要： The interaction and complex metabolism of microorganisms in cocoa pulp drive the fermentation process. To investigate this, four strains from spontaneous cocoa fermentation, including Hanseniaspora uvarum, Saccharomyces cerevisiae, Lactiplantibacillus plantarum, and Gluconobacter potus were combined to ferment cocoa pulp. Nineteen machine learning algorithms were run with the dataset of volatile compounds quantified by headspace solid-phase microextraction gas chromatography-mass spectrometry (HS-SPME-GC-MS) against integrated olfactory evaluation to reveal metabolite-sensory attribute relationships. The models showed high prediction accuracy, ranging from 0.85 for sourness by Gradient Boost Machine to 0.28 for sweetness by linear regression. Ethyl esters, specifically ethyl octanoate and ethyl 9-decenoate, were found positive for aroma development. Polynomial regression, neural network modeling and gradient boosting decision trees highlighted the high carbohydrate consumption rate of S. cerevisiae, the pectin degradation ability of H. uvarum, and the synergy of lactic acid bacteria with G. potus. This study offers new insights into cocoa flavor and the development of fermentation starter cocktails.
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