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

The impact of cocoa lipid content on chocolate quality has been extensively described. Nevertheless, few studies have elucidated the cocoa lipid composition and their bioactive properties, focusing only on specific lipids. In the present study the lipidome of fine-flavor cocoa fermentation was analyzed using LC-MS-QTOF and a Machine Learning model to assess potential bioactivity was developed. Our results revealed that the cocoa lipidome, comprised mainly of fatty acyls and glycerophospholipids, remains stable during fine-flavor cocoa fermentations. Also, several Machine Learning algorithms were trained to explore potential biological activity among the identified lipids. We found that K-Nearest Neighbors had the best performance. This model was used to classify the identified lipids as bioactive or non-bioactive, nominating 28 molecules as potential bioactive lipids. None of these compounds have been previously reported as bioactive. Our work is the first untargeted lipidomic study and systematic effort to investigate potential bioactivity in fine-flavor cocoa lipids.

Dissecting fine-flavor cocoa bean fermentation through metabolomics analysis to break down the current metabolic paradigm.

Cocoa fermentation plays a crucial role in producing flavor and bioactive compounds of high demand for food and nutraceutical industries. Such fermentations are frequently described as a succession of three main groups of microorganisms (i.e., yeast, lactic acid, and acetic acid bacteria), each producing a relevant metabolite (i.e., ethanol, lactic acid, and acetic acid). Nevertheless, this view of fermentation overlooks two critical observations: the role of minor groups of microorganisms to produce valuable compounds and the influence of environmental factors (other than oxygen availability) on their biosynthesis. Dissecting the metabolome during spontaneous cocoa fermentation is a current challenge for the rational design of controlled fermentations. This study evaluates variations in the metabolic fingerprint during spontaneous fermentation of fine flavor cocoa through a multiplatform metabolomics approach. Our data suggested the presence of two phases of differential metabolic activity that correlate with the observed variations on temperature over fermentations: an exothermic and an isothermic phase. We observed a continuous increase in temperature from day 0 to day 4 of fermentation and a significant variation in flavonoids and peptides between phases. While the second phase, from day four on, was characterized for lower metabolic activity, concomitant with small upward and downward fluctuations in temperature. Our work is the first to reveal two phases of metabolic activity concomitant with two temperature phases during spontaneous cocoa fermentation. Here, we proposed a new paradigm of cocoa fermentation that considers the changes in the global metabolic activity over fermentation, thus changing the current paradigm based only on three main groups of microorganism and their primary metabolic products.

Dissecting industrial fermentations of fine flavour cocoa through metagenomic analysis.

The global demand for fine-flavour cocoa has increased worldwide during the last years. Fine-flavour cocoa offers exceptional quality and unique fruity and floral flavour attributes of high demand by the world's elite chocolatiers. Several studies have highlighted the relevance of cocoa fermentation to produce such attributes. Nevertheless, little is known regarding the microbial interactions and biochemistry that lead to the production of these attributes on farms of industrial relevance, where traditional fermentation methods have been pre-standardized and scaled up. In this study, we have used metagenomic approaches to dissect on-farm industrial fermentations of fine-flavour cocoa. Our results revealed the presence of a shared core of nine dominant microorganisms (i.e. Limosilactobacillus fermentum, Saccharomyces cerevisiae, Pestalotiopsis rhododendri, Acetobacter aceti group, Bacillus subtilis group, Weissella ghanensis group, Lactobacillus_uc, Malassezia restricta and Malassezia globosa) between two farms located at completely different agro-ecological zones. Moreover, a community metabolic model was reconstructed and proposed as a tool to further elucidate the interactions among microorganisms and flavour biochemistry. Our work is the first to reveal a core of microorganisms shared among industrial farms, which is an essential step to process engineering aimed to design starter cultures, reducing fermentation times, and controlling the expression of undesirable phenotypes.