Lipidomic profiling of bioactive lipids during spontaneous fermentations of fine-flavor cocoa

Herrera‐Rocha, Fabio; Cala, Mónica P.; León-Inga, Ana Maria; Mejía, Jenny Lorena Aguirre; Rodríguez-López, Claudia; Florez, Sergio; Chica, María José; Olarte, Héctor Hugo; Duitama, Jorge; Barrios, Andrés Fernando González; Fernández-Niño, Miguel

doi:10.1016/j.foodchem.2022.133845

Cited by 6 publications

(7 citation statements)

References 29 publications

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“…Our results seem to disagree with the data published by Herrera-Rocha et al (2022) and also by Servent et al (2018) . In the former, the authors concluded that there is no change in the content of lipids during fermentation, including glycerophospholipids, the second most abundant group of lipids in fermenting cocoa.…”

Section: Discussioncontrasting

confidence: 99%

“…This leads us to believe that similar to other transformations that occur during cocoa fermentation, the changes in the lipid fraction are directly dependent on the composition of the microbiota involved and indicates that, in the case of the present study, some change in the composition of glycerophospholipids is to be expected. As there are no details on the fermentative microbiota in Herrera-Rocha et al (2022) , nor in Herrera-Rocha et al (2021) nor in Servent et al (2018) , the proof of this hypothesis will depend on additional experiments.…”

Section: Discussionmentioning

confidence: 99%

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Unraveling potential enzymes and their functional role in fine cocoa beans fermentation using temporal shotgun metagenomics

Lima

Castro²,

Solar³

et al. 2022

Front. Microbiol.

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Cocoa beans fermentation is a spontaneous process, essential for the generation of quality starting material for fine chocolate production. The understanding of this process has been studied by the application of high-throughput sequencing technologies, which grants a better assessment of the different microbial taxa and their genes involved in this microbial succession. The present study used shotgun metagenomics to determine the enzyme-coding genes of the microbiota found in two different groups of cocoa beans varieties during the fermentation process. The statistical evaluation of the most abundant genes in each group and time studied allowed us to identify the potential metabolic pathways involved in the success of the different microorganisms. The results showed that, albeit the distinction between the initial (0 h) microbiota of each varietal group was clear, throughout fermentation (24–144 h) this difference disappeared, indicating the existence of selection pressures. Changes in the microbiota enzyme-coding genes over time pointed to the distinct ordering of fermentation at 24–48 h (T1), 72–96 h (T2), and 120–144 h (T3). At T1, the significantly more abundant enzyme-coding genes were related to threonine metabolism and those genes related to the glycolytic pathway, explained by the abundance of sugars in the medium. At T2, the genes linked to the metabolism of ceramides and hopanoids lipids were clearly dominant, which are associated with the resistance of microbial species to extreme temperatures and pH values. In T3, genes linked to trehalose metabolism, related to the response to heat stress, dominated. The results obtained in this study provided insights into the potential functionality of microbial community succession correlated to gene function, which could improve cocoa processing practices to ensure the production of more stable quality end products.

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Section: Discussioncontrasting

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Unraveling potential enzymes and their functional role in fine cocoa beans fermentation using temporal shotgun metagenomics

Lima

Castro²,

Solar³

et al. 2022

Front. Microbiol.

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show abstract

“…The most relevant change through the processing chain is in sweet compounds, which decrease considerably during the process. This drop is associated with a decrease in the carbohydrate content during the processing chain 53,54 , as most of these molecules are reported as sweet agents. In the roasted samples some compounds linked to sour and bitter showed a higher abundance, but the real impact of these suggested flavor molecules still needs to be elucidated.…”

Section: Molecular Flavor Prediction For Compounds Involved In the Pr...mentioning

confidence: 94%

“…Previous studies have annotated around 210 compounds during the fermentation, drying, and roasting of fine-flavor cocoa 53,54 . However, for less than half of these compounds a flavor profile has been reported.…”

Section: Molecular Flavor Prediction For Compounds Involved In the Pr...mentioning

confidence: 99%

FlavorMiner: A Machine Learning Platform for Extracting Molecular Flavor Profiles from Structural Data

Herrera-Rocha,

Fernández-Niño,

Duitama

et al. 2024

Preprint

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Flavor is the main factor driving consumers acceptance of food products. However, tracking the biochemistry of flavor is a formidable challenge due to the complexity of food composition. Current methodologies for linking individual molecules to flavor in foods and beverages are expensive and time-consuming. Predictive models based on machine learning (ML) are emerging as an alternative to speed up this process. Nonetheless, the optimal approach to predict flavor features of molecules remains elusive. In this work we present FlavorMiner, an ML-based multilabel flavor predictor. FlavorMiner seamlessly integrates different combinations of algorithms and mathematical representations, augmented with class balance strategies to address the inherent class of the input dataset. Notably, Random Forest and K-Nearest Neighbors combined with Extended Connectivity Fingerprint and RDKit molecular descriptors consistently outperform other combinations in most cases. Resampling strategies surpass weight balance methods in mitigating bias associated with class imbalance. FlavorMiner exhibits remarkable accuracy, with an average ROC AUC score of 0.88. This algorithm was used to analyze cocoa metabolomics data, unveiling its profound potential to help extract valuable insights from intricate food metabolomics data. FlavorMiner can be used for flavor mining in any food product, drawing from a diverse training dataset that spans over 934 distinct food products.

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“…Lipidomics, developed from metabolomics, is useful for elucidating lipid composition based on lipid characteristics. Previous studies showed that high-resolution mass spectrometry (HRMS)-based untargeted lipidomics can obtain comprehensive information for the lipid profile and FA precursors in fermented products. , Nevertheless, more current studies focused on the effects of bioactive lipids on human health and presented a shallow level of exploration using non-target lipidomics. Furthermore, the dynamic changes of lipid molecules, especially the key lipids that contributed to producing the characteristic flavor of fermented fish, are not clear during fermentation.…”

Section: Introductionmentioning

confidence: 99%

Analysis of Lipid Molecule Profiling and Conversion Pathway in Mandarin Fish (Siniperca chuatsi) during Fermentation via Untargeted Lipidomics

Chen

Zhang

Huang

et al. 2023

J. Agric. Food Chem.

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Lipids are the key aroma contributors and nutrients in fermented fish products. A total of 376 lipid molecules were identified in mandarin fish during fermentation by untargeted lipidomics, including glycerolipids, glycerophospholipids, lysoglycerophospholipid, sphingolipids, fatty acids (FAs), and sterol lipids. Both lipid composition and content changed dynamically during fermentation. Triglyceride (TAG, 30.05%) and phosphatidylcholine (PC, 14.87%) were the two major lipids, with especially 39.36% saturated FAs in PCs and 35.34% polyunsaturated FAs in TAGs. The content of TAGs and PCs reached a peak point at 0 and 6 days, respectively. Fermented mandarin fish expressed a high nutritional value, and the ratio of total linoleic acid/total linolenic acid was about 5:1. Glycerophospholipid metabolism was a potential metabolic pathway, and the oxidation of derived FAs contributed to flavor. These data progress in understanding lipid dynamic variation during fermentation and provide thoughts on controlling the flavor quality and safety of fermented fish products.

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Lipidomic profiling of bioactive lipids during spontaneous fermentations of fine-flavor cocoa

Cited by 6 publications

References 29 publications

Unraveling potential enzymes and their functional role in fine cocoa beans fermentation using temporal shotgun metagenomics

Unraveling potential enzymes and their functional role in fine cocoa beans fermentation using temporal shotgun metagenomics

FlavorMiner: A Machine Learning Platform for Extracting Molecular Flavor Profiles from Structural Data

Analysis of Lipid Molecule Profiling and Conversion Pathway in Mandarin Fish (Siniperca chuatsi) during Fermentation via Untargeted Lipidomics

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