Quorum Sensing Signalling Molecules in Cocoa Pulp Wine Fermentation: A Systematic Review

Quorum Sensing Signalling Molecules in Cocoa Pulp Wine Fermentation: A Systematic Review | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Systematic Review Quorum Sensing Signalling Molecules in Cocoa Pulp Wine Fermentation: A Systematic Review Anthony Oppong Kyekyeku, Margaret Owusu, John Edem Kongor, Daniel Sitsofe Yabani This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9583044/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Cocoa pulp wine fermentation is governed by a dynamic succession of yeasts, lactic acid bacteria (LAB), and acetic acid bacteria (AAB) whose metabolic interactions determine ethanol yield, acidification trajectories, aroma precursor formation, and final beverage quality. Despite growing interest in precision fermentation, the role of quorum sensing (QS) — the density-dependent chemical communication that coordinates collective microbial behaviour — remains poorly characterised in cocoa pulp fermentation systems compared to other multispecies food fermentations. This systematic review synthesises current evidence on QS biomolecules relevant to bacteria and fungi, with the aim of establishing a mechanistic framework for understanding how interspecies signalling shapes fermentation performance and metabolite evolution in cocoa pulp wine. Following a PRISMA-compliant literature search across Scopus, PubMed, and ScienceDirect (1985–2025), peer-reviewed studies characterising QS molecules, signalling pathways, and their functional roles in food fermentation ecosystems were identified, screened, and narratively synthesised. Evidence was integrated across bacterial systems — acyl-homoserine lactones (AHLs) in Gram-negative bacteria, LuxS-mediated autoinducer-2 (AI-2) signalling in LAB — and fungal systems involving tyrosol and farnesol in yeasts, with comparative contextualisation from dairy, kimchi, and kombucha fermentations. The synthesis reveals that QS-mediated signalling regulates critical fermentation phenotypes including acidification kinetics, bacteriocin production, stress tolerance, biofilm formation, and cross-kingdom metabolic coordination in mixed-species consortia. Key evidence gaps persist regarding in situ QS signal quantification in cocoa pulp matrices, the functional consequences of polyphenol-mediated signal quenching, and the translation of QS dynamics into starter culture design. LC–MS/MS is identified as the essential analytical platform for resolving QS molecule detection in complex food matrices. This review provides a foundational framework for integrating QS-guided microbial intelligence into precision cocoa fermentation strategies, opening a high-impact research frontier for the development of reproducible, high-value cocoa-based beverages. Applied & Industrial Microbiology General Microbiology Food Science & Technology Mycology Analytical Chemistry quorum sensing cocoa pulp juice fermentation cocoa pulp wine microbial succession lactic acid bacteria yeasts acyl-homoserine lactones autoinducer-2 farnesol tyrosol cross-kingdom signalling precision fermentation LC-MS/MS analysis Figures Figure 1 Figure 2 1. Introduction Microbial communication is a fundamental determinant of behaviour, metabolic coordination, and ecological structuring in fermented food systems. Among the most influential communication mechanisms is quorum sensing (QS), a density-dependent regulatory process in which microorganisms synthesise, release, and detect small extracellular signalling molecules to coordinate collective physiological responses. Put simply, QS works much like a microbial headcount — bacteria and fungi constantly release tiny chemical signals into their surroundings, essentially broadcasting their presence to neighbouring cells. When enough of these signals accumulate, indicating that the population has grown sufficiently large, the entire community responds in unison, switching on shared behaviours that no single cell could achieve alone. It is, in effect, the difference between a lone individual whispering in an empty room and a crowd speaking with one voice. First characterised in marine Vibrio species as autoinducer-mediated control of bioluminescence [ 3 , 11 ], QS is now recognised as a ubiquitous regulatory architecture spanning Gram-negative and Gram-positive bacteria and fungi alike. Through these systems, cells modulate gene expression governing nutrient acquisition, stress response, extracellular enzyme production, metabolite biosynthesis, and biofilm formation [ 12 , 49 , 56 ] — processes that directly determine fermentation efficiency, product safety, and final beverage quality. The multi-kingdom consortia that inhabit fermented foods present an especially rich context for QS research, because signals from bacteria, yeasts, and fungi intersect and co-regulate the succession dynamics that shape product quality. In food fermentations, QS plays a central role in determining microbial succession dynamics, the emergence of dominant species, and the metabolic transitions that shape product quality. This has been demonstrated across ecosystems as diverse as fermented vegetables [ 22 ], dairy cultures [ 23 ], kombucha consortia [ 30 ], and meat processing environments where biofilm-mediated community behaviour strongly influences spoilage trajectories [ 15 , 42 ]. Within these multi-kingdom consortia, Gram-negative bacteria primarily communicate through acyl-homoserine lactones (AHLs) [ 12 , 43 ], whereas Gram-positive species employ peptide pheromones governed by two-component signalling networks [ 26 ]. Concurrently, fungal species such as Saccharomyces cerevisiae and Candida albicans produce aromatic alcohols — most notably farnesol and tyrosol — that regulate morphogenesis, stress response, and population density control [ 18 , 6 ]. These cross-kingdom signalling interactions collectively shape fermentation kinetics, product safety, and the organoleptic properties of fermented foods, yet the extent to which they operate as an integrated communication network in any single fermentation system remains incompletely understood. Cocoa pulp wine fermentation represents precisely this gap. Despite growing interest in cocoa pulp as a substrate for high-value beverage production, QS mechanisms remain among the least characterised regulatory layers in this system. Cocoa pulp supports sequential colonisation by yeasts, LAB, and AAB, each contributing to ethanol production, acidification, and aroma precursor generation respectively, through a succession that mirrors the community architecture seen in other well-studied QS-active fermentations [ 19 , 45 , 47 , 66 ]. Evidence that luxS-encoded AI-2 pathways are conserved — and in some strains mutated — in cocoa-associated LAB [ 20 , 21 ] provides the first molecular indication that QS shapes LAB dominance trajectories during fermentation. Yet no study has directly detected, quantified, or functionally characterised QS molecules within a cocoa pulp wine fermentation context. This absence represents a critical gap: without knowing which signals are present, at what concentrations, and at which fermentation stages, the design of precision starter cultures for cocoa wine production remains constrained to empirical trial rather than mechanistic rationale. Closing this gap requires both a rigorous synthesis of current QS evidence from relevant fermentation systems and a clear analytical roadmap for future experimental work. Liquid chromatography-tandem mass spectrometry (LC–MS/MS) has emerged as the reference platform for QS molecule detection in complex food matrices [ 4 ], yet its application to polyphenol-rich cocoa pulp environments presents specific matrix challenges that must be addressed. Equally, integrating QS dynamics with digital fermentation monitoring, real-time telemetry, and predictive modelling offers a pathway toward precision-controlled cocoa wine production that moves beyond the variability inherent in spontaneous fermentation. This systematic review therefore addresses the following aim: to integrate current mechanistic evidence on QS biomolecules in bacteria and fungi and evaluate their demonstrated roles in food fermentation ecosystems. Specifically, the review establishes a comparative framework for understanding how interspecies chemical communication governs fermentation performance, metabolite evolution, and sensory quality in cocoa pulp wine; and identifies the specific knowledge gaps and analytical priorities that must be resolved to translate QS science into practical fermentation control for high-value cocoa-based beverages. 2. Methods 2.1 Review Design and Protocol This review was conducted following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) framework to ensure methodological transparency, reproducibility, and rigour. The review protocol was developed a priori and adhered to established standards for comprehensive evidence synthesis in food microbiology. No statistical meta-analysis was conducted given the mechanistic heterogeneity of included studies; a narrative synthesis approach was applied throughout. 2.2 Search Strategy and Information Sources A structured literature search was executed across three major scientific databases — Scopus, PubMed, and ScienceDirect — covering publications from 1985, when foundational QS studies first appeared [ 24 ], to December 2025. Web of Science was also searched; however, the advanced field-code search interface was inaccessible during the review period, and this constitutes a recognised limitation of the search strategy. Coverage of the core food science and microbiology literature was ensured through the three databases searched, which collectively index the primary journals in this field. Boolean search terms were designed to capture the full breadth of QS modalities and their relevance to fermentation systems, including: "quorum sensing", "autoinducer-2", "acyl-homoserine lactones", "fungal quorum sensing", "lactic acid bacteria quorum sensing", "cocoa pulp juice fermentation", "microbial succession", "fermented foods", "farnesol", "tyrosol", "LuxI/LuxR systems", "AHL detection", "LAB signaling peptides", and "cross-kingdom interactions". Additional filters restricted retrieval to peer-reviewed journal articles in English, excluding conference abstracts, non-scientific communications, preprints without peer review, and predatory journal publications. Reference lists of all eligible studies were manually screened to capture 12 additional relevant articles not retrieved through database queries. The database searches yielded 699 records in total: Scopus (n = 470), PubMed (n = 54), and ScienceDirect (n = 175). Combined with 12 records identified through reference list screening, 711 records were identified for screening. After removal of 86 cross-database duplicates and 68 records dated 2026 that fell outside the 1985–2025 date range (total exclusions at deduplication stage: 154 records), 557 unique records were retained for title and abstract screening. The overlap rate between databases was approximately 12%, which is lower than typical food science systematic review norms; this reflects the complementary rather than redundant coverage of the three databases for this topic. 2.3 Eligibility Criteria Title and abstract screening were conducted independently against predefined eligibility criteria. Studies were included if they: (i) characterised bacterial or fungal QS mechanisms; (ii) examined QS roles in fermented food ecosystems; (iii) reported experimental detection, quantification, or functional evaluation of QS molecules; or (iv) analysed microbial succession and metabolite transformations relevant to cocoa or fruit-based fermentations. Studies were excluded if they lacked primary data, focused solely on clinical pathogens without ecological relevance to food systems, or did not provide mechanistic insights into microbial communication. Of the 557 records screened at the title and abstract stage, 453 were excluded — primarily for focusing on clinical or environmental pathogens without food fermentation relevance (n ≈ 142), non-QS microbiology topics (n ≈ 78), or cocoa agronomy without fermentation context (n ≈ 33). The remaining 104 records were retrieved as full texts and assessed against the eligibility criteria. Full-text exclusions totalled 43 records: 20 lacked mechanistic QS data, 14 addressed non-food fermentation systems, and 9 were secondary reviews without primary QS evidence. Studies were appraised for methodological quality, clarity of QS detection techniques, ecological relevance to fermentation systems, and mechanistic depth, with emphasis on reports employing LC–MS, GC–MS, NMR, bioassays, genetic, and transcriptomic approaches [ 4 ]. 2.4 Data Extraction and Synthesis A total of 61 studies met all eligibility criteria and were included in the narrative synthesis. These comprised 28 studies characterising bacterial QS mechanisms, 18 addressing fungal QS and cross-kingdom signalling, 15 focused on cocoa fermentation microbiology with QS relevance, and 11 reporting analytical methods for QS molecule detection in complex food matrices. Several studies contributed to more than one category — particularly the analytical methods papers, which also reported primary mechanistic data falling within the bacterial QS, fungal QS, or cocoa fermentation themes — and were therefore counted in each applicable category; the category totals consequently sum to more than 61 unique included studies. Data extraction followed a structured template capturing: (i) QS molecules identified; (ii) microbial species involved; (iii) signalling pathways characterised; (iv) functional phenotypes regulated (e.g., biofilm formation, acid production, stress tolerance); (v) fermentation context; and (vi) analytical or genetic techniques employed. Where applicable, results from related studies were triangulated to resolve discrepancies or strengthen mechanistic interpretations. Given the mechanistic heterogeneity of included studies, no statistical meta-analysis was conducted; a narrative synthesis approach was applied throughout, with comparative analyses employed to contextualise cocoa pulp fermentation within broader food ecology literature. 3. Results and Discussion 3.1 Bacterial Quorum Sensing in Food Fermentation Ecosystems Bacterial QS constitutes a central regulatory mechanism coordinating collective behaviours through the production, release, and detection of extracellular signalling molecules. In food fermentation ecosystems, QS modulates metabolic coordination, stress adaptation, flavour precursor formation, and the spatial–temporal structuring of microbial consortia [ 10 , 23 , 25 , 55 ]. The fundamental architecture spans AHL circuits in Gram-negative bacteria and peptide-mediated systems in Gram-positive species, enabling microbial communities to synchronise gene expression in response to population density or environmental cues [ 12 , 17 , 26 ]. Understanding the mechanistic basis of these systems — and the specific phenotypes they regulate — is the prerequisite for applying QS knowledge to fermentation control in cocoa pulp wine. 3.1.1 AHL-Mediated Signalling in Gram-Negative Fermentation Bacteria Gram-negative bacteria employ AHLs as primary QS autoinducers, synthesised by LuxI-type enzymes and detected by cognate LuxR-type receptors, forming a transcriptional activation architecture whose discovery in Vibrio fischeri established the foundational QS paradigm [ 12 , 16 , 43 , 46 , 59 ]. The structural diversity of AHL acyl chains — ranging from C4 to C18 with variable 3-oxo and 3-hydroxy substituents — confers inter-strain and inter-species signalling specificity [ 8 , 13 , 51 ]. In food systems, AHL-mediated QS has been demonstrated in spoilage-associated Pseudomonas spp. [ 29 ], in Erwinia spp. during plant material fermentation [ 48 ], and in acetic acid bacteria (AAB) of the genus Gluconacetobacter, which produce AHLs that regulate cooperative metabolism and oxidative activity [ 45 ]. These signalling interactions govern exopolysaccharide production, motility, enzyme secretion, and biofilm formation — phenotypes directly relevant to AAB behaviour during the oxidative phase of cocoa pulp fermentation, where acetic acid production must be precisely modulated to prevent over-acidification of the wine matrix. A limitation of current evidence is that most AHL characterisation in AAB has been conducted in laboratory monocultures rather than in the competitive, polyphenol-containing, low-pH environments characteristic of cocoa pulp; the degree to which AHL signalling is quenched or preserved under these conditions remains to be established experimentally. 3.1.2 AI-2 Signalling in Lactic Acid Bacteria The LuxS/AI-2 system represents the most broadly distributed QS modality among LAB and the most directly evidenced in cocoa-associated microorganisms. AI-2 is synthesised via the LuxS enzyme and functions as a universal interspecies signal capable of mediating cross-species communication between Gram-positive and Gram-negative bacteria [ 7 , 35 , 58 ]. In LAB, AI-2 activity is functionally linked to acid tolerance, biofilm maturation, bacteriocin synthesis, and competitive colonisation of fermentation substrates [ 31 , 37 , 44 ]. The direct relevance to cocoa pulp wine is anchored by two key molecular findings: luxS gene homologs have been identified in cocoa-associated Lactiplantibacillus plantarum and Limosilactobacillus fermentum strains [ 19 ], and a frameshift mutation in luxS was documented in cocoa-derived L. fermentum 87 [ 20 ] — demonstrating that QS pathway variation at the strain level may explain observed differences in fermentation performance between genotypically similar LAB isolates. Furthermore, luxS expression is upregulated in Lactobacillus spp. under acid stress [ 37 ], suggesting that AI-2 signalling functions as a stress-coordination mechanism during the pH transition phase of cocoa pulp fermentation. Although the universality of AI-2 has been questioned — some bacterial genera lack functional AI-2 receptor systems [ 48 ] — evidence from fermented food ecosystems consistently supports AI-2 as an ecologically active signal in multispecies LAB consortia. These observations collectively support the hypothesis that AI-2-mediated LAB communication actively structures the timing and extent of the acidification phase — a hypothesis that has not yet been tested through direct QS molecule quantification in cocoa pulp wine matrices. 3.1.3 Peptide-Based QS in Gram-Positive Bacteria Gram-positive LAB regulate community behaviours through auto-inducing peptides (AIPs) acting via two-component signal transduction systems, governing bacteriocin production, proteolysis, adhesion, and surface colonisation [ 26 , 27 , 36 , 39 ]. Nisin production by Lactococcus lactis, plantaricin regulation in Lactiplantibacillus plantarum, and Agr-type peptide signalling in staphylococci illustrate the functional breadth of peptide QS across food environments [ 14 , 33 , 40 , 41 , 57 ]. The competitive relevance of peptide QS to cocoa pulp fermentation is substantial: in a substrate populated simultaneously by multiple LAB strains, yeasts, and AAB, bacteriocin-mediated competitive exclusion — regulated by peptide QS — may determine which LAB lineages dominate the mid-fermentation window and thereby dictate the organic acid balance and flavour precursor profile of the resulting wine. Specifically, nisin and plantaricin production can suppress non-LAB competitors and steer succession toward homofermentative or heterofermentative outcomes with direct consequences for the lactic/acetic acid ratio and overall wine stability. The challenge is that most peptide QS studies in LAB have been conducted in dairy or vegetable fermentation matrices; whether the highly acidic, polyphenol-rich, and ethanol-accumulating environment of cocoa pulp fermentation alters peptide signal stability or receptor sensitivity has not been investigated. 3.2 Fungal Quorum Sensing and Cross-Kingdom Interactions Building on the bacterial QS evidence, a complete picture of communication dynamics in cocoa pulp wine fermentation requires integrating the fungal dimension. Yeasts dominate the early anaerobic phase and are the primary drivers of ethanol yield, CO₂ evolution, and initial flavour compound formation — making their density-dependent signalling behaviours directly consequential for fermentation trajectory. 3.2.1 Farnesol and Tyrosol as Fungal QS Molecules Farnesol, the first QS molecule identified in a eukaryote, accumulates extracellularly during Candida albicans growth and inhibits the yeast-to-hypha morphological transition at threshold concentrations, suppressing biofilm formation and modulating population structure [ 9 , 18 , 52 ]. In food fermentation environments, farnesol's impact on sterol biosynthesis and membrane integrity may alter volatile compound formation trajectories relevant to cocoa wine organoleptic profile [ 32 ]. Tyrosol, the functionally antagonistic second fungal QS molecule in C. albicans [ 6 ], promotes germ tube formation and cooperative proliferation under sugar-rich, nitrogen-limited conditions — conditions structurally analogous to early-phase cocoa pulp fermentation [ 2 ]. The reciprocal regulation of yeast morphogenesis by these two signals establishes a density-sensitive switch whose net outcome depends on signal ratio and timing. In food yeasts beyond Candida, including Saccharomyces cerevisiae, non-pathogenic cocoa fermentation species such as Hanseniaspora uvarum and Pichia kudriavzevii produce aromatic alcohols including phenylethanol and tryptophol that regulate pseudohyphal growth and stress responses under nutrient restriction [ 1 ]. A critical limitation of the current evidence base is that farnesol and tyrosol have been characterised almost exclusively in pathogenic Candida contexts; their production kinetics, effective concentrations, and receptor systems in these non-pathogenic cocoa fermentation yeasts are largely unknown. 3.2.2 Cross-Kingdom Signalling and Fermentation Phase Transitions Cross-kingdom QS interactions represent perhaps the most consequential and least characterised dimension of cocoa pulp wine fermentation. Farnesol suppresses AHL-mediated bacterial QS networks in Pseudomonas aeruginosa by interfering with receptor binding [ 28 ], while bacterial AI-2 signals modulate yeast carbohydrate consumption rates and stress resilience [ 37 ]. In the sequential ecology of cocoa pulp fermentation — yeast-dominated ethanol production, LAB-driven acidification, AAB-governed oxidative phase — these reciprocal signals may accelerate or retard phase transitions in ways currently attributed solely to substrate depletion and pH change. Genome sequencing of cocoa-associated LAB strains has identified luxS gene homologs and QS-associated loci across fermentation-relevant species [ 19 , 20 ], providing molecular evidence that the signalling machinery for cross-species communication is present in the cocoa ecosystem. Whether these pathways are transcriptionally active and functionally consequential during cocoa pulp wine fermentation remains the central unanswered question. Addressing it would fundamentally reframe our understanding of succession dynamics in this system and open the possibility of rational signal manipulation as a precision fermentation tool. 3.3 Synthesis of QS Evidence Across Fermentation Systems Table 1 summarises the principal QS molecules, organisms, regulated phenotypes, and analytical detection methods identified across the 61 studies included in this review, with explicit notation of evidence available from cocoa-relevant fermentation contexts. QS molecule Organism class Key regulated phenotypes Detection method Cocoa/pulp evidence?† AHLs (C4–C18) Gram-negative bacteria (AAB, Pseudomonas, Erwinia) Biofilm, enzyme secretion, oxidative metabolism LC–MS/MS, MRM, bioassay Indirect (AAB genomics) AI-2 (DPD) LAB, Gram-negative bacteria Acid tolerance, bacteriocin, biofilm, stress response LC–MS/MS, V. harveyi bioassay Direct (luxS in cocoa LAB) [20, 21] AIPs (peptides) Gram-positive LAB Bacteriocin production, protease, adhesion HPLC, bioassay, genetics Inferred (bacteriocin-mediated LAB competition) Farnesol Candida spp., yeasts Morphogenesis, biofilm, sterol biosynthesis LC–MS/MS, HPLC-fluorescence Not yet detected in cocoa pulp Tyrosol Candida spp., S. cerevisiae Germ tube formation, growth promotion HPLC, LC–MS/MS Not yet detected in cocoa pulp Phenylethanol / tryptophol S. cerevisiae Pseudohyphal growth, stress tolerance GC–MS, HPLC Indirectly relevant (cocoa yeasts) † Evidence strength categories Direct = detected/quantified in cocoa fermentation contexts; Indirect = inferred from genomics or closely analogous fermentation systems; Inferred = mechanistically plausible based on organism presence but not experimentally tested. This synthesis reveals a clear asymmetry in the evidence base. Bacterial QS — particularly AI-2 in LAB — is the best-evidenced pathway in cocoa-relevant contexts, with direct molecular data from cocoa-associated strains [ 19 , 20 , 21 ]. AHL-mediated signalling in AAB has genomic and ecological plausibility but lacks in situ detection data from cocoa pulp matrices. Fungal QS molecules (farnesol, tyrosol) have extensive mechanistic characterisation in Candida biology but zero direct detection in any cocoa fermentation context. This asymmetry defines both the current state of knowledge and the specific experimental priorities for the field. 3.4 Established Biochemical Pathway Frameworks in Cocoa Fermentation and the Missing Regulatory Layer The evidence synthesised in Sections 3.1 – 3.3 establishes QS as a mechanistically credible regulatory system in cocoa pulp wine fermentation. However, QS does not operate in isolation — it sits within, and must be understood in relation to, the broader biochemical pathway frameworks that already describe how carbon, energy, and metabolites flow through this fermentation ecosystem. Four major frameworks currently govern scientific understanding of cocoa fermentation biochemistry. Each is rigorously supported by high-quality experimental evidence. Each also shares a fundamental limitation: none incorporates a microbial communication layer. Identifying precisely where QS fits within — and extends — these existing frameworks is the conceptual contribution of this section. 3.4.1 The Three-Phase Succession Model: The Dominant Paradigm The foundational framework for cocoa fermentation biochemistry is the three-phase microbial succession model, established through decades of culture-dependent and culture-independent community profiling by De Vuyst, Weckx, Schwan, Wheals, and their collaborators [ 45 , 50 , 65 ]. In this model, fermentation proceeds through three overlapping but distinguishable phases: an anaerobic yeast-dominant phase (0–48 h) in which glucose and fructose are converted to ethanol and CO2; a microaerophilic LAB-dominant phase (24–72 h) in which residual sugars and citrate are converted to lactic acid, acetic acid, mannitol, and diacetyl; and an aerobic AAB-dominant phase (48–120 + h) in which ethanol is oxidised to acetic acid through the sequential action of pyrroquinoline quinone-dependent alcohol dehydrogenase and acetaldehyde dehydrogenase [ 27 ]. This succession model has been reproduced across fermentation systems in Ghana, Brazil, Ecuador, Malaysia, and Ivory Coast, and forms the basis of the starter culture development programme led by De Vuyst and colleagues at Vrije Universiteit Brussel — the most comprehensive and authoritative body of experimental work in the field [ 50 ]. The succession model is descriptively powerful. It accurately predicts the broad temporal ordering of microbial dominance and the major metabolite trajectories. Its limitation, however, is explanatory rather than descriptive: it attributes phase transitions to substrate depletion and oxygen availability — passive, physicochemical triggers — rather than to active, cell-density-dependent coordination. The model tells us what transitions occur and roughly when; it does not explain how the microbial community collectively decides when to transition or how individual species coordinate their metabolic outputs across guild boundaries. This explanatory gap is precisely the space that QS occupies. 3.4.2 Carbon Flux and Fluxome Models: Metabolic Precision Without Communication A second and more mechanistically detailed class of frameworks uses metabolic flux analysis to quantify how carbon moves through specific enzymatic pathways within individual species and mixed communities under cocoa pulp simulation conditions. The core fluxome work of Adler, De Vuyst, and colleagues used 13C isotope labelling to map carbon flux through the Embden-Meyerhof-Parnas (EMP) pathway, the phosphoketolase (PPK) pathway, and citrate metabolism in LAB strains isolated from Ghanaian cocoa fermentations [ 62 ]. The complementary AAB fluxome analysis revealed that Acetobacter pasteurianus operates primarily through the pentose phosphate pathway (PPP) and gluconeogenesis rather than glycolysis, with lactate functioning not only as an energy source but as a carbon precursor for PPP-derived reducing equivalents [ 62 , 67 ]. Together, these fluxome models revealed that the metabolic interdependence of LAB and AAB — LAB supplying both ethanol and lactate, AAB requiring both for optimal acetate production — is more tightly coupled than the succession model implies. The fluxome frameworks are quantitatively precise at the single-species and pairwise level but face a fundamental limitation in mixed-community contexts: 13C-based metabolic flux analysis cannot easily resolve individual species contributions in complex consortia, and it captures only instantaneous metabolic states rather than the dynamic regulatory transitions between states [ 62 ]. Critically, the fluxome models contain no mechanism by which a cell knows what its neighbours are doing. A yeast cell producing ethanol has no representation in the fluxome model of whether the LAB population has reached a density at which AI-2 signalling is active or whether the AAB population has crossed the AHL quorum threshold. The fluxome describes the metabolic output of communication events; it does not describe the communication events themselves. 3.4.3 Kinetic and Mathematical Models: Predicting Without Sensing A third framework class encompasses the mathematical and kinetic models of cocoa fermentation developed by Osborne, Morales-Contreras, and others, formulated as systems of coupled ordinary differential equations describing the temporal evolution of metabolite concentrations — glucose, fructose, ethanol, lactic acid, acetic acid — and microbial population sizes [ 63 , 64 ]. These models have been trained and validated on fermentation time series data from multiple producing regions and are capable of quantitatively reproducing microbial succession and metabolite kinetics under defined conditions. A Bayesian parameter estimation framework has further enabled reverse-engineering of environmental conditions from model parameters, opening an avenue toward condition discrimination between fermentation protocols [ 63 ]. The kinetic models are powerful predictive tools within their parametric domain. However, they share a structural assumption that defines their scope and their limitation: microbial growth rates and metabolite conversion kinetics are modelled as functions of substrate concentrations, pH, temperature, and oxygen availability — all physicochemical state variables. The models contain no biological communication terms. Phase transitions are triggered by substrate thresholds and oxygen flux, not by quorum threshold crossings. As a consequence, these models cannot distinguish between a fermentation that transitions from yeast to LAB dominance because glucose is depleted and one that transitions because an AI-2 quorum threshold has been crossed and LAB have coordinately upregulated acid stress tolerance genes. Both produce the same metabolite trajectory in the model; only one reflects the actual biological mechanism. Adding QS signal concentration terms as state variables to these kinetic frameworks would represent a fundamental architectural advance — one that this review provides the mechanistic basis for. 3.4.4 The Shetty Proline-Linked Pentose Phosphate Pathway Framework: A Substrate-Side Redox Model A fourth and conceptually distinct framework is the proline-linked pentose phosphate pathway (PLPPP) model proposed by Shetty and Wahlqvist, developed principally in the context of phenolic phytochemical biosynthesis and microbial biotransformation of plant-derived substrates [ 60 , 61 ]. This model proposes that proline cycling — the reversible interconversion of proline and pyrroline-5-carboxylate mediated by proline dehydrogenase — acts as a cellular redox valve that stimulates flux through the oxidative pentose phosphate pathway (ox-PPP). Increased PPP flux generates NADPH and erythrose-4-phosphate; erythrose-4-phosphate feeds the shikimate pathway, which is the entry point to the phenylpropanoid pathway responsible for phenolic compound biosynthesis. The model predicts that microbial or environmental stressors that increase proline cycling will simultaneously increase PPP activity, phenolic mobilisation, and antioxidant enzyme response — a coupled redox-phenolic network that has been validated in LAB fermentation of plant matrices including barley, legumes, sweet potatoes, and fruit substrates. The PLPPP framework is directly relevant to cocoa pulp wine fermentation for two reasons. First, cocoa pulp is a polyphenol-rich substrate — containing catechins, procyanidins, and anthocyanins at concentrations of 6–8% w/v — meaning that the substrate-side redox dynamics described by Shetty’s model are likely active throughout fermentation as microbial species interact with and biotransform the cocoa phenolic matrix. Second, and most importantly for this review, the same polyphenolic compounds that the PLPPP model identifies as products of PPP-driven biosynthesis are also the primary quenchers of QS signals in the cocoa pulp matrix: anthocyanins and procyanidins promote lactonase-mimetic AHL hydrolysis, and catechins competitively inhibit AI-2 receptor binding [ 45 ]. This creates a bidirectional interface between Shetty’s substrate-side redox model and the QS communication framework that has not previously been described in cocoa fermentation literature. 3.4.5 The Bidirectional Polyphenol–QS–Redox Interface: A Novel Conceptual Bridge The juxtaposition of these four frameworks with the QS evidence synthesised in this review reveals a regulatory circuit that none of the existing models describes. The circuit operates as follows. During early fermentation, cocoa pulp polyphenols — present at high initial concentrations and actively biotransformed by yeasts — quench AHL signals through lactonase-mimetic hydrolysis and competitive receptor inhibition, reducing effective QS signal availability at the AAB quorum threshold [ 45 ]. This polyphenol-mediated quenching delays AAB community coordination, extending the yeast-dominant phase and protecting early ethanol accumulation from premature oxidation. As yeast metabolism and LAB acidification progressively degrade and biotransform the polyphenol pool — reducing catechin and procyanidin concentrations through enzymatic oxidation and acid hydrolysis — the polyphenol buffering capacity for QS quenching diminishes. AHL signals from AAB populations begin to accumulate above the quorum threshold, triggering coordinated upregulation of oxidative metabolism and biofilm formation at oxygen interfaces. Concurrently, AI-2 from LAB reaches signalling concentrations, coordinating the acidification phase. The sequential resolution of polyphenol quenching thus functions as a natural timing mechanism for QS-mediated phase transitions — one that is entirely invisible to the succession model, the fluxome frameworks, and the kinetic ODE models. This circuit has a further dimension in the PLPPP context. Shetty’s model predicts that microbial stress — including acid stress, ethanol toxicity, and oxidative stress during fermentation — increases proline cycling and PPP flux in fermenting microorganisms, driving phenolic biotransformation and antioxidant enzyme response. If increased PPP flux simultaneously increases production of phenolic compounds from the residual substrate pool, this would provide positive feedback on QS quenching during stress phases — a mechanism by which the fermentation community may collectively dampen inter-kingdom communication signals during periods of maximum metabolic stress, only releasing QS coordination once stress is partially resolved by substrate depletion. Whether this feedback is biologically significant in cocoa pulp wine fermentation is an open empirical question; but its theoretical coherence — grounded in established mechanisms from both the PLPPP and QS literatures — makes it a testable hypothesis that can be addressed in controlled fermentation trials through paired LC–MS/MS QS signal quantification and polyphenol profiling at defined fermentation time points. 3.4.6 Constructing the QS Pathway Framework for Cocoa Pulp Wine Fermentation Drawing on the evidence synthesised in Sections 3.1 – 3.3 and the framework analysis above, a conceptual QS pathway framework can now be constructed for this specific fermentation ecosystem. The framework has four components that operate simultaneously across the three fermentation phases (Fig. 2 ). The first component is the polyphenol quenching envelope — a time-varying suppression function on QS signal availability determined by the concentration and composition of cocoa pulp polyphenols at each fermentation stage. This envelope is highest at fermentation onset and decreases progressively as polyphenols are biotransformed, determining the temporal window within which QS signals can accumulate to threshold concentrations. It interfaces directly with Shetty’s PLPPP model, as PPP-driven phenolic biotransformation rates in LAB and yeasts determine the rate at which the quenching envelope contracts. The second component is the intra-kingdom QS layer — the density-dependent signalling circuits within each microbial guild. Yeast farnesol and tyrosol circuits operate within the yeast population to regulate population density, morphogenesis, and ester biosynthesis during Phase I. AI-2 and peptide AIP circuits operate within the LAB population to coordinate acidification rate, bacteriocin production, and acid stress tolerance during Phase II. AHL circuits operate within the AAB population to gate oxidative metabolism during Phase III. Each of these intra-kingdom circuits is directly regulated by the polyphenol quenching envelope, with the degree of regulation depending on the structural specificity of the interaction between the signal molecule and the polyphenolic compound. The third component is the cross-kingdom QS interface — the inter-guild signalling interactions described in Section 3.2.2 . Yeast-derived farnesol suppresses premature AAB biofilm formation, functioning as a cross-kingdom gating signal for the Phase II–III transition. AI-2 from LAB may modulate yeast carbohydrate assimilation rates, influencing ethanol yield during Phase I. These cross-kingdom signals add regulatory bandwidth beyond what any single-guild intra-kingdom circuit can achieve, enabling the community to coordinate the timing of phase transitions as an integrated ecological system rather than as independent populations responding to common environmental cues. The fourth component is the metabolite output layer — the sensory-relevant metabolites whose production rates are directly determined by the state of the QS regulatory layers above. Ester yield and aromatic character are downstream of the yeast farnesol/tyrosol circuit; acidity profile and mouthfeel are downstream of the LAB AI-2 and peptide AIP circuits; volatile acidity and oxidative aroma character are downstream of the AAB AHL circuit and cross-kingdom farnesol gating. The mechanistic linkages in this layer were detailed in Section 3.7 [Table 2 ] and provide the measurable endpoints against which the QS pathway framework can be experimentally validated in controlled fermentation trials. Figure 2 presents this QS pathway framework conceptually, showing the four components, their interactions, and their relationship to the phase structure of the fermentation. The framework is explicitly positioned as an extension of, not a replacement for, the succession model, fluxome, and kinetic ODE frameworks. It adds the regulatory communication layer that those frameworks lack, providing the mechanistic basis for predicting why phase transitions occur at specific times under specific conditions — a question that substrate depletion and oxygen availability alone cannot answer. In this sense, the QS pathway framework proposed here constitutes the missing fourth dimension of cocoa pulp fermentation modelling: alongside the ecological dimension (succession), the biochemical dimension (carbon flux), and the kinetic dimension (population dynamics), it provides the communication dimension that governs the timing, coordination, and quality consequences of the transitions between all three. 3.5 Analytical Detection of QS Molecules in Complex Food Matrices The asymmetry identified in Table 1 is partly a reflection of analytical accessibility rather than mechanistic absence. Cocoa pulp presents a chemically hostile matrix for QS detection: polyphenolic compounds — anthocyanins, catechins, and procyanidins — can quench AHL activity through lactonase-mimetic hydrolysis (enzymatic-like ring-opening of the AHL lactone moiety) and competitive receptor inhibition, potentially masking signals or producing ion suppression during mass spectrometric analysis [ 5 , 45 , 53 , 54 ]. The acidic pH of fermenting cocoa pulp (pH 3.5–5.0) additionally promotes AHL lactone ring hydrolysis, necessitating matrix-matched stabilisation protocols during sample preparation. These challenges explain in part why QS detection in cocoa systems has so far been limited to genomic inference rather than direct measurement. LC–MS/MS has emerged as the validated reference platform for multi-analyte QS profiling in complex biological matrices, offering the sensitivity and structural resolution required to simultaneously detect AHLs, AI-2 via DPD derivatisation, and fungal aromatic alcohols from a single sample aliquot [ 4 , 34 , 38 ]. In analogous food systems where such analysis has been applied, the results have been informative: AI-2 activity detected in kimchi LAB populations correlated with bacteriocin-driven competitive shifts during succession [ 22 ]; AHL profiling in dairy matrices identified strain-specific signalling windows linked to biofilm formation and protease activity peaks [ 23 ]; and fungal aromatic alcohol monitoring in wine fermentations revealed density-dependent tyrosol accumulation patterns that preceded yeast morphological transitions [ 1 ]. Each of these findings was only possible because the analytical method was validated against the relevant food matrix — a step that has not yet been taken for cocoa pulp wine. The practical roadmap for establishing cocoa pulp wine QS analytics involves the following sequential steps: 1. Adapt existing LC–MS/MS MRM workflows to cocoa pulp matrix-matched calibration standards with solid-phase extraction cleanup to address polyphenol interference. 2. Develop a DPD derivatisation protocol for AI-2 quantification in acidic pulp fermentates. 3. Include deuterated internal standards for AHLs and aromatic alcohols to correct for matrix-dependent ion suppression. 4. Sample at defined fermentation time points — 0, 12, 24, 48, 72, 96, and 144 h — aligned with known microbial succession windows to capture signal dynamics rather than single-point snapshots. 3.6 Implications for Precision Fermentation and Starter Culture Design The evidence synthesised across Sections 3.1 – 3.5 converges on a single practical proposition: QS signalling in cocoa pulp wine fermentation is a mechanistically active regulatory layer that, once characterised, could be deliberately engaged as a fermentation control tool. The strongest immediate application lies in starter culture design. Selecting LAB strains with characterised high AI-2 production would serve to accelerate and synchronise the acidification transition, reducing the window during which the fermentation is vulnerable to spoilage-associated community shifts. Co-inoculating yeast strains with defined aromatic alcohol production profiles would allow modulation of cross-kingdom interactions during the anaerobic phase. Monitoring AHL concentration as a process indicator of AAB community activation during the oxidative phase would provide a molecular trigger for timing aeration and temperature interventions. These applications are not speculative — each has a direct precedent in other fermented beverage systems, including starter culture development in cocoa bean fermentation [ 50 ] and QS-informed LAB consortia design in dairy fermentations [ 23 ]. A critical evidence gap, however, constrains translation from principle to practice: no study has yet demonstrated a causal link between a specific QS molecule, its concentration dynamics, and a measurable fermentation outcome in cocoa pulp wine. Table 1 shows that the most relevant QS pathways are supported by indirect or inferred evidence in cocoa-specific contexts. Closing this gap requires the experimental programme outlined in Section 3.5 — in situ QS signal quantification throughout cocoa pulp wine fermentation — as the essential first step. Until that data exists, QS-informed starter culture design for cocoa wine must be treated as an evidence-based hypothesis rather than an established protocol. The research priorities are clear: in situ detection, functional bioassay validation, transcriptomic characterisation of QS-regulated gene expression in cocoa-associated strains, and integration of QS time-series data with metabolomic and sensory endpoints to establish the empirical linkages that will enable translation to practice. 3.7 Quorum Sensing as a Mechanistic Determinant of Sensory Quality in Cocoa Pulp Wine The ultimate purpose of understanding QS in cocoa pulp wine fermentation is not mechanistic completeness per se, but the ability to engineer a beverage with defined, reproducible sensory attributes — flavour, aroma, acidity, and mouthfeel — from a substrate historically characterised by quality variability. This connection between QS signalling and sensory outcome is not merely inferential; it is mechanistically traceable through the metabolic pathways that each QS modality controls at each fermentation phase. Establishing these linkages is the scientific foundation upon which QS-guided starter culture design and predictive fermentation modelling must ultimately rest. During the yeast-dominant phase, farnesol and tyrosol regulate population density, morphological switching, and the balance between ethanol production and ester biosynthesis. At physiologically relevant concentrations, farnesol suppresses filamentation and redirects yeast metabolic flux toward ester-producing pathways, favouring the accumulation of ethyl acetate, isoamyl acetate, and 2-phenylethyl acetate — volatile esters that contribute the fruity, floral aromatic character considered desirable in cocoa pulp wine [ 18 , 6 ]. Tyrosol, by promoting cooperative proliferation under nitrogen-limited conditions typical of late-stage cocoa pulp, sustains yeast biomass and prolongs ester synthesis beyond what would occur under unregulated density decline [ 2 ]. The ratio of farnesol to tyrosol therefore functions as a molecular dial governing the aromatic foundation of the wine: high farnesol relative to tyrosol during early fermentation promotes ester accumulation and fruity character, while imbalance in either direction risks either premature yeast senescence (loss of aroma complexity) or unchecked filamentation (off-flavour formation). Because these signals are density-dependent and substrate-sensitive, their dynamics in cocoa pulp will differ from the Candida pathogenic contexts in which they have been characterised, underscoring the need for direct measurement in this matrix. During the LAB-dominant acidification phase, AI-2-mediated coordination governs the rate and trajectory of lactic acid accumulation, with direct consequences for the acidity and mouthfeel of the finished wine. AI-2 upregulation under acid stress [ 37 ] suggests that the signal functions as a community-level stress integrator: as pH falls and lactic acid accumulates, AI-2 production increases, reinforcing coordinated acid tolerance responses and sustaining LAB metabolic activity into the lower pH range. The practical sensory consequence is that QS-competent LAB strains — those with intact luxS and functional AI-2 signalling — are likely to drive more complete, controlled acidification than QS-deficient strains, producing wines with cleaner acidity profiles and lower residual sugar. The frameshift mutation in luxS documented in cocoa-derived L. fermentum 87 [ 20 ] implies that this strain may exhibit less coordinated acidification behaviour, contributing to the batch-to-batch pH variability observed in spontaneous cocoa pulp fermentations. Bacteriocin production, also regulated through peptide QS circuits [ 26 , 27 ], further shapes mouthfeel and microbial safety by suppressing off-flavour-producing contaminants — a function with direct organoleptic implications not yet characterised in cocoa pulp wine. The AAB-dominated oxidative phase presents the most direct and consequential QS-sensory linkage in cocoa pulp wine production. AHL accumulation above the quorum threshold in Acetobacter and Gluconacetobacter populations coordinates the upregulation of alcohol dehydrogenase and aldehyde dehydrogenase expression, driving the oxidation of ethanol to acetaldehyde and subsequently to acetic acid [ 12 , 43 , 45 ]. The concentration and timing of this transition determine volatile acidity — the single most important sensory defect threshold in wine-style fermented beverages. Controlled, QS-gated AAB oxidation produces moderate acetic acid levels that contribute complexity and preservation character to cocoa wine; uncontrolled or premature AHL-triggered oxidation produces vinegar-like harshness that renders the product unacceptable. Critically, farnesol produced by the residual yeast population during this phase may suppress premature AAB biofilm formation at oxygen interfaces [ 28 ], acting as a cross-kingdom brake on oxidative metabolism. This yeast-derived QS signal therefore functions as a natural gating mechanism for acetic acid accumulation — a regulatory interaction that, if confirmed through direct detection in cocoa pulp wine, would explain much of the inter-batch volatile acidity variability currently attributed to uncontrolled aeration. Table 2 summarises these direct mechanistic linkages between the major QS signals, the metabolic pathways they regulate, and the sensory attributes they govern across the three phases of cocoa pulp wine fermentation. QS signal Phase Metabolic pathway regulated Sensory attribute governed Failure mode if QS disrupted Farnesol Phase I (Yeast-dominant) Suppresses filamentation; redirects flux to ester biosynthesis (ethyl acetate, isoamyl acetate, 2-phenylethyl acetate) Fruity, floral aroma; ester-alcohol balance Flat or solvent-like aroma; premature yeast senescence; reduced ester formation Tyrosol Phase I (Yeast-dominant) Promotes cooperative proliferation; sustains ethanol and secondary metabolite production under N-limitation Ethanol yield; body and mouthfeel; sustained aroma synthesis Thin mouthfeel; incomplete sugar conversion; loss of aromatic complexity AI-2 (LuxS/AI-2) Phase II (LAB-dominant) Coordinates acidification rate, acid stress tolerance, bacteriocin synthesis, and competitive exclusion of spoilage organisms Perceived acidity; clean vs. sharp sourness; mouthfeel smoothness; microbial safety Uncontrolled acidification; variable pH; spoilage proliferation; harsh or inconsistent tartness AIPs (peptide QS) Phase II (LAB-dominant) Nisin/plantaricin-mediated spoilage exclusion; protease activation; LAB succession structuring Flavour cleanness; absence of biogenic amines; structural mouthfeel from proteolytic activity Off-flavour from spoilage; biogenic amine accumulation; thin or unstructured mouthfeel AHLs (C4-C18) Phase III (AAB-oxidative) Gates alcohol/aldehyde dehydrogenase expression; coordinates O2-interface biofilm; regulates ethanol-to-acetic acid conversion rate Volatile acidity level; aroma complexity (acetaldehyde, acetate esters); oxidative character Vinegar-like harshness from over-acidification; or flat aroma from insufficient oxidative contribution Farnesol (cross-kingdom) Yeast to AAB transition Suppresses premature AAB biofilm at O2 interfaces; gates onset of oxidative metabolism Timing and degree of volatile acidity; phase transition sharpness Premature oxidation; acetic acid overshoot; loss of fruity esters; vinegar character Table 2 . Mechanistic linkages between QS signals, regulated metabolic pathways, and sensory attributes in cocoa pulp wine fermentation across the three fermentation phases, with failure modes under QS disruption. Phase I = yeast-dominant alcoholic phase; Phase II = LAB-dominant acidification phase; Phase III = AAB-oxidative phase. Taken together, these linkages establish that the sensory quality of cocoa pulp wine — its fruity aromatic character, acidity profile, mouthfeel, and volatile balance — is not simply the emergent result of substrate chemistry and temperature. It is, in substantial part, the phenotypic output of QS-governed metabolic decisions made by the microbial community at defined population density thresholds. This reframing has an important practical consequence: sensory consistency cannot be fully achieved by controlling physicochemical fermentation parameters alone. It requires understanding — and ultimately controlling — the chemical communication layer that drives the metabolic decisions underlying those parameters. The mechanistic linkages established in Table 2 , grounded in the QS evidence synthesised across Sections 3.1 – 3.6 and measurable through the analytical roadmap in Section 3.5 , provide the scientific foundation for that control. Their translation into QS-guided starter culture design and predictive fermentation modelling represents the direct next step in this research programme. 4. Conclusion This systematic review establishes quorum sensing as a mechanistically plausible and biotechnologically tractable regulatory layer in cocoa pulp wine fermentation. AHL-mediated signalling in acetic acid bacteria, LuxS/AI-2 circuits in lactic acid bacteria, and aromatic alcohol-based signalling in fermentation yeasts each represent functional QS modalities that are active in closely related food fermentation systems and encoded in cocoa-associated microbial genomes. Cross-kingdom interactions between these systems add a further dimension of regulatory complexity that has not yet been experimentally mapped in cocoa pulp wine contexts. The central gap identified by this review is not a lack of relevant QS biology but a lack of direct measurement in cocoa pulp matrices. Closing this gap requires the deployment of validated LC–MS/MS analytical protocols adapted to the polyphenol-rich cocoa matrix, combined with targeted functional studies linking QS signal dynamics to fermentation phase transitions and metabolite trajectories. When such data become available, QS profiling is positioned to serve as both a scientific tool for understanding microbial ecology and a practical instrument for precision fermentation control. For cocoa-producing regions such as Ghana — responsible for approximately 20% of global cocoa supply — where fermentation quality is a determinant of both export value and smallholder livelihoods, the translation of QS-guided fermentation science into accessible starter culture and monitoring technologies represents a high-impact research frontier. Cocoa pulp wine, produced from a substrate historically discarded as waste, is positioned to become a model system for biodigital terroir: a scientifically optimised, traceably controlled, and culturally grounded fermented beverage product. Abbreviations AAB — Acetic Acid Bacteria; AHL — Acyl-Homoserine Lactone; AI-2 — Autoinducer-2; AIP — Autoinducing Peptide; CCST — CSIR College of Science and Technology; DPD — 4,5-dihydroxy-2,3-pentanedione; GC — Gas Chromatography; HPLC — High-Performance Liquid Chromatography; LAB — Lactic Acid Bacteria; LC — Liquid Chromatography; LC-MS/MS — Liquid Chromatography–Tandem Mass Spectrometry; MRM — Multiple Reaction Monitoring; MS — Mass Spectrometry; ODE — Ordinary Differential Equation; PPP — Pentose Phosphate Pathway; PRISMA — Preferred Reporting Items for Systematic Reviews and Meta-Analyses; QS — Quorum Sensing. Declarations Funding: No external funding was received for this work. Conflict of Interest: The authors declare no conflict of interest. Consent for publication: Not applicable. Author Contributions: Conceptualization, Anthony Oppong Kyekyeku, Margaret Owusu, John Edem Kongor and Daniel Sitsofe Yabani; Methodology, Margaret Owusu, John Edem Kongor and Daniel Sitsofe Yabani; Validation, Margaret Owusu, John Edem Kongor and Daniel Sitsofe Yabani; Writing – Original Draft Preparation, Anthony Oppong Kyekyeku; Writing – Review & Editing, Anthony Oppong Kyekyeku, Margaret Owusu, John Edem Kongor and Daniel Sitsofe Yabani; Supervision, Margaret Owusu, John Edem Kongor and Daniel Sitsofe Yabani. Data Availability: Not applicable (review article; no primary datasets generated). Ethical Approval: Not applicable. Acknowledgements: The authors acknowledge all who provided guidance and support during the development of the research programme underpinning this work. Use of AI tools: The authors used Anthropic’s Claude AI assistant to support language polishing, structural review, and reference formatting during manuscript preparation. 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Process Biochemistry, 39(7), 789–804. https://doi.org/10.1016/S0032-9592(03)00121-2 Shetty, K., & Wahlqvist, M.L. (2004). A model for the role of the proline-linked pentose-phosphate pathway in phenolic phytochemical biosynthesis and mechanism of action for human health and environmental applications. Asia Pacific Journal of Clinical Nutrition, 13(1), 1–24. Adler, P., Frey, L.J., Berger, A., Bolten, C.J., Hansen, C.E., & Wittmann, C. (2013). Core fluxome and metafluxome of lactic acid bacteria under simulated cocoa pulp fermentation conditions. Applied and Environmental Microbiology, 79(18), 5670–5681. https://doi.org/10.1128/AEM.01483-13 Morales-Contreras, B.E., Weckx, S., & De Vuyst, L. (2022). Exploring cocoa bean fermentation mechanisms by kinetic modelling. Royal Society Open Science, 9(2), 210274. https://doi.org/10.1098/rsos.210274 Osborne, J.P., & Edwards, C.G. (2018). A mathematical model of cocoa bean fermentation. Royal Society Open Science, 5(10), 180964. https://doi.org/10.1098/rsos.180964 Schwan, R.F., & Wheals, A.E. (2004). The microbiology of cocoa fermentation and its role in chocolate quality. Critical Reviews in Food Science and Nutrition, 44(4), 205–221. https://doi.org/10.1080/10408690490464104 Morales, I., Moreira-González, A.R., & Galán-Vara, F. (2021). Dissecting fine-flavor cocoa bean fermentation through metabolomics analysis to break down the current metabolic paradigm. Scientific Reports, 11, 21904. https://doi.org/10.1038/s41598-021-01427-8 Adler, P., Bolten, C.J., Dohnt, K., Hansen, C.E., & Wittmann, C. (2014). The key to acetate: metabolic fluxes of acetic acid bacteria under cocoa pulp fermentation-simulating conditions. Applied and Environmental Microbiology, 80(15), 4702–4716. https://doi.org/10.1128/AEM.01048-14 Additional Declarations The authors declare no competing interests. Supplementary Files PRISMA1.docx PRISMA 2020 Checklist — Completed RESEAR1.docx Research Highlights Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-9583044","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Systematic Review","associatedPublications":[],"authors":[{"id":632811606,"identity":"104a99f1-583d-478b-93f9-65a463cab714","order_by":0,"name":"Anthony Oppong Kyekyeku","email":"data:image/png;base64,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","orcid":"https://orcid.org/0009-0003-1642-9178","institution":"CSIR College of Science and Technology","correspondingAuthor":true,"prefix":"","firstName":"Anthony","middleName":"Oppong","lastName":"Kyekyeku","suffix":""},{"id":632811607,"identity":"58d32983-2d4f-4cb1-a506-887ee8a7aed1","order_by":1,"name":"Margaret Owusu","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAxklEQVRIiWNgGAWjYPACGyjNBsQSxGlJA2Jm0rQcJkGLbnuPmeTPtvPy/P3nDzB8KDvMIB/dgF+L2ZkzZtK8bbcNZ9xIZmCcce4wg+GdAwS03MjdJs3YdjvBQIKZgZm3DahlRgJhLUCHnUsw4D/MwPyXWC0SvG0HEgwYkhmYGYFa5CUIaTlz/rM1z7lkkF8MDvacS+cxIKjleFvizR9ldsAQO/jwwY8yazl5Qg5DAQeAmMfgAAk6IEC+gWQto2AUjIJRMMwBAL5QQdrc3dolAAAAAElFTkSuQmCC","orcid":"","institution":"CSIR College of Science and Technology","correspondingAuthor":true,"prefix":"","firstName":"Margaret","middleName":"","lastName":"Owusu","suffix":""},{"id":632811608,"identity":"8989c688-017d-4912-aed7-b229ed594378","order_by":2,"name":"John Edem Kongor","email":"data:image/png;base64,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","orcid":"https://orcid.org/0000-0002-6575-5362","institution":"CSIR Food Research Institute","correspondingAuthor":true,"prefix":"","firstName":"John","middleName":"Edem","lastName":"Kongor","suffix":""},{"id":632811609,"identity":"2b2b7af8-b608-4ff1-8d8c-b4bc2a95d032","order_by":3,"name":"Daniel Sitsofe Yabani","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA7UlEQVRIie3QsWoCQRCA4ZGFvRSjtneccK8wcnAQPDBPc5ZaqYXFgZDrUpvHCHmBlYG7RlsJaKGNdSRBBEFcFDTVql2K/YrdYeBvBsCy/iMFIPSH1fN8Iu9LvPTRBEhdd+akorD+0xksamExXY1/IW4HL85saUo8haE/ytcYTVrENUiePxT2yJSQQvJRMkZfEtgFpnqKiXsjCfd4YAxHDySRX35lJFfC+FsnATi5MfFYdhvvb4zuJAEGSogESmNSKYaf886Wm9UsF5tdP6Ygy9bGBMTT9TwC9UyMxoNpzvIylnb6CdI/G8uyLEs7Ai7XRYShCmv7AAAAAElFTkSuQmCC","orcid":"","institution":"Cocoa Research Institute of Ghana","correspondingAuthor":true,"prefix":"","firstName":"Daniel","middleName":"Sitsofe","lastName":"Yabani","suffix":""}],"badges":[],"createdAt":"2026-05-01 05:41:36","currentVersionCode":1,"declarations":{"humanSubjects":false,"vertebrateSubjects":false,"conflictsOfInterestStatement":false,"humanSubjectEthicalGuidelines":false,"humanSubjectConsent":false,"humanSubjectClinicalTrial":false,"humanSubjectCaseReport":false,"vertebrateSubjectEthicalGuidelines":false},"doi":"10.21203/rs.3.rs-9583044/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-9583044/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":108492953,"identity":"0d169d1f-fc97-4a55-9e42-1bb3c5a01705","added_by":"auto","created_at":"2026-05-05 09:59:05","extension":"jpeg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":469854,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003ePRISMA 2020 flow diagram for the systematic review of quorum sensing biomolecules in cocoa pulp wine fermentation. Total records identified: n = 711 (Scopus n = 470; PubMed n = 54; ScienceDirect n = 175; hand search n = 12). After removal of 86 cross-database duplicates and 68 out-of-range (2026) records (total excluded at deduplication stage: n = 154), 557 unique records were retained for title and abstract screening. Following full-text assessment (n = 104), 61 studies met eligibility criteria for inclusion in four categories: bacterial QS mechanisms (n = 28), fungal QS and cross-kingdom signalling (n = 18), cocoa fermentation microbiology (n = 15), and analytical methods (n = 11). Web of Science was attempted but inaccessible; this constitutes an acknowledged search limitation. Seven additional framework references incorporated during conceptual synthesis (Section 3.4) are not counted in the PRISMA evidence base.\u003c/em\u003e\u003c/p\u003e","description":"","filename":"Figure1PRISMAflowdiagram.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-9583044/v1/be40fa766c836f90ed8d9d48.jpeg"},{"id":108409352,"identity":"5ad6bd47-cb06-4e16-9308-abe5c83355b5","added_by":"auto","created_at":"2026-05-04 09:59:45","extension":"jpeg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":470016,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003eConceptual QS pathway framework for cocoa pulp wine fermentation. Four components operate simultaneously: (1) the polyphenol quenching envelope — a time-varying suppression function on QS signal availability that contracts as polyphenols are biotransformed, interfacing with the Shetty proline-linked pentose phosphate pathway (PLPPP) model; (2) intra-kingdom QS circuits within each microbial guild — farnesol/tyrosol in yeasts (Phase I), AI-2 and peptide AIPs in LAB (Phase II), and AHLs in AAB (Phase III); (3) the cross-kingdom QS interface governing phase transitions, including farnesol-mediated suppression of premature AAB oxidation and AI-2 modulation of yeast carbohydrate metabolism; (4) the QS-governed metabolite output layer linking each signal to specific sensory attributes. The framework extends the De Vuyst–Weckx three-phase succession model by adding the communication dimension that governs transition timing. ADH = alcohol dehydrogenase; ALDH = aldehyde dehydrogenase; PLPPP = proline-linked pentose phosphate pathway.\u003c/em\u003e\u003c/p\u003e","description":"","filename":"Figure2QSpathwayframework.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-9583044/v1/fd74a8c90885d493a7c3db2f.jpeg"},{"id":109069246,"identity":"ee2dda0c-e90b-45cd-873a-fece1a852c35","added_by":"auto","created_at":"2026-05-12 10:21:54","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1341470,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9583044/v1/702f5f46-790e-4d0d-a325-13d8b673d6e8.pdf"},{"id":108409349,"identity":"13e54625-0e59-4b29-af73-b43a87e64791","added_by":"auto","created_at":"2026-05-04 09:59:44","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":34355,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003ePRISMA 2020 Checklist — Completed\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"PRISMA1.docx","url":"https://assets-eu.researchsquare.com/files/rs-9583044/v1/7f1f4833d05d4fe4aba6f947.docx"},{"id":108409351,"identity":"98580b21-8e30-4ca2-9b74-b17e294f6432","added_by":"auto","created_at":"2026-05-04 09:59:44","extension":"docx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":27124,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eResearch Highlights\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"RESEAR1.docx","url":"https://assets-eu.researchsquare.com/files/rs-9583044/v1/35cbc4e5dc7477d2f34efcb5.docx"}],"financialInterests":"The authors declare no competing interests.","formattedTitle":"\u003cp\u003e\u003cstrong\u003eQuorum Sensing Signalling Molecules in Cocoa Pulp Wine Fermentation: A Systematic Review\u003c/strong\u003e\u003c/p\u003e","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eMicrobial communication is a fundamental determinant of behaviour, metabolic coordination, and ecological structuring in fermented food systems. Among the most influential communication mechanisms is quorum sensing (QS), a density-dependent regulatory process in which microorganisms synthesise, release, and detect small extracellular signalling molecules to coordinate collective physiological responses. Put simply, QS works much like a microbial headcount \u0026mdash; bacteria and fungi constantly release tiny chemical signals into their surroundings, essentially broadcasting their presence to neighbouring cells. When enough of these signals accumulate, indicating that the population has grown sufficiently large, the entire community responds in unison, switching on shared behaviours that no single cell could achieve alone. It is, in effect, the difference between a lone individual whispering in an empty room and a crowd speaking with one voice. First characterised in marine Vibrio species as autoinducer-mediated control of bioluminescence [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e], QS is now recognised as a ubiquitous regulatory architecture spanning Gram-negative and Gram-positive bacteria and fungi alike. Through these systems, cells modulate gene expression governing nutrient acquisition, stress response, extracellular enzyme production, metabolite biosynthesis, and biofilm formation [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e, \u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e56\u003c/span\u003e] \u0026mdash; processes that directly determine fermentation efficiency, product safety, and final beverage quality.\u003c/p\u003e \u003cp\u003eThe multi-kingdom consortia that inhabit fermented foods present an especially rich context for QS research, because signals from bacteria, yeasts, and fungi intersect and co-regulate the succession dynamics that shape product quality. In food fermentations, QS plays a central role in determining microbial succession dynamics, the emergence of dominant species, and the metabolic transitions that shape product quality. This has been demonstrated across ecosystems as diverse as fermented vegetables [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e], dairy cultures [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e], kombucha consortia [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e], and meat processing environments where biofilm-mediated community behaviour strongly influences spoilage trajectories [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e, \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e]. Within these multi-kingdom consortia, Gram-negative bacteria primarily communicate through acyl-homoserine lactones (AHLs) [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e], whereas Gram-positive species employ peptide pheromones governed by two-component signalling networks [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. Concurrently, fungal species such as Saccharomyces cerevisiae and Candida albicans produce aromatic alcohols \u0026mdash; most notably farnesol and tyrosol \u0026mdash; that regulate morphogenesis, stress response, and population density control [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. These cross-kingdom signalling interactions collectively shape fermentation kinetics, product safety, and the organoleptic properties of fermented foods, yet the extent to which they operate as an integrated communication network in any single fermentation system remains incompletely understood.\u003c/p\u003e \u003cp\u003eCocoa pulp wine fermentation represents precisely this gap. Despite growing interest in cocoa pulp as a substrate for high-value beverage production, QS mechanisms remain among the least characterised regulatory layers in this system. Cocoa pulp supports sequential colonisation by yeasts, LAB, and AAB, each contributing to ethanol production, acidification, and aroma precursor generation respectively, through a succession that mirrors the community architecture seen in other well-studied QS-active fermentations [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e, \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e, \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e, \u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e66\u003c/span\u003e]. Evidence that luxS-encoded AI-2 pathways are conserved \u0026mdash; and in some strains mutated \u0026mdash; in cocoa-associated LAB [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e] provides the first molecular indication that QS shapes LAB dominance trajectories during fermentation. Yet no study has directly detected, quantified, or functionally characterised QS molecules within a cocoa pulp wine fermentation context. This absence represents a critical gap: without knowing which signals are present, at what concentrations, and at which fermentation stages, the design of precision starter cultures for cocoa wine production remains constrained to empirical trial rather than mechanistic rationale.\u003c/p\u003e \u003cp\u003eClosing this gap requires both a rigorous synthesis of current QS evidence from relevant fermentation systems and a clear analytical roadmap for future experimental work. Liquid chromatography-tandem mass spectrometry (LC\u0026ndash;MS/MS) has emerged as the reference platform for QS molecule detection in complex food matrices [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e], yet its application to polyphenol-rich cocoa pulp environments presents specific matrix challenges that must be addressed. Equally, integrating QS dynamics with digital fermentation monitoring, real-time telemetry, and predictive modelling offers a pathway toward precision-controlled cocoa wine production that moves beyond the variability inherent in spontaneous fermentation.\u003c/p\u003e \u003cp\u003eThis systematic review therefore addresses the following aim: to integrate current mechanistic evidence on QS biomolecules in bacteria and fungi and evaluate their demonstrated roles in food fermentation ecosystems. Specifically, the review establishes a comparative framework for understanding how interspecies chemical communication governs fermentation performance, metabolite evolution, and sensory quality in cocoa pulp wine; and identifies the specific knowledge gaps and analytical priorities that must be resolved to translate QS science into practical fermentation control for high-value cocoa-based beverages.\u003c/p\u003e"},{"header":"2. Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1 Review Design and Protocol\u003c/h2\u003e \u003cp\u003eThis review was conducted following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) framework to ensure methodological transparency, reproducibility, and rigour. The review protocol was developed a priori and adhered to established standards for comprehensive evidence synthesis in food microbiology. No statistical meta-analysis was conducted given the mechanistic heterogeneity of included studies; a narrative synthesis approach was applied throughout.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2 Search Strategy and Information Sources\u003c/h2\u003e \u003cp\u003eA structured literature search was executed across three major scientific databases \u0026mdash; Scopus, PubMed, and ScienceDirect \u0026mdash; covering publications from 1985, when foundational QS studies first appeared [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e], to December 2025. Web of Science was also searched; however, the advanced field-code search interface was inaccessible during the review period, and this constitutes a recognised limitation of the search strategy. Coverage of the core food science and microbiology literature was ensured through the three databases searched, which collectively index the primary journals in this field. Boolean search terms were designed to capture the full breadth of QS modalities and their relevance to fermentation systems, including: \"quorum sensing\", \"autoinducer-2\", \"acyl-homoserine lactones\", \"fungal quorum sensing\", \"lactic acid bacteria quorum sensing\", \"cocoa pulp juice fermentation\", \"microbial succession\", \"fermented foods\", \"farnesol\", \"tyrosol\", \"LuxI/LuxR systems\", \"AHL detection\", \"LAB signaling peptides\", and \"cross-kingdom interactions\". Additional filters restricted retrieval to peer-reviewed journal articles in English, excluding conference abstracts, non-scientific communications, preprints without peer review, and predatory journal publications. Reference lists of all eligible studies were manually screened to capture 12 additional relevant articles not retrieved through database queries.\u003c/p\u003e \u003cp\u003eThe database searches yielded 699 records in total: Scopus (n\u0026thinsp;=\u0026thinsp;470), PubMed (n\u0026thinsp;=\u0026thinsp;54), and ScienceDirect (n\u0026thinsp;=\u0026thinsp;175). Combined with 12 records identified through reference list screening, 711 records were identified for screening. After removal of 86 cross-database duplicates and 68 records dated 2026 that fell outside the 1985\u0026ndash;2025 date range (total exclusions at deduplication stage: 154 records), 557 unique records were retained for title and abstract screening. The overlap rate between databases was approximately 12%, which is lower than typical food science systematic review norms; this reflects the complementary rather than redundant coverage of the three databases for this topic.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3 Eligibility Criteria\u003c/h2\u003e \u003cp\u003eTitle and abstract screening were conducted independently against predefined eligibility criteria. Studies were included if they: (i) characterised bacterial or fungal QS mechanisms; (ii) examined QS roles in fermented food ecosystems; (iii) reported experimental detection, quantification, or functional evaluation of QS molecules; or (iv) analysed microbial succession and metabolite transformations relevant to cocoa or fruit-based fermentations. Studies were excluded if they lacked primary data, focused solely on clinical pathogens without ecological relevance to food systems, or did not provide mechanistic insights into microbial communication.\u003c/p\u003e \u003cp\u003eOf the 557 records screened at the title and abstract stage, 453 were excluded \u0026mdash; primarily for focusing on clinical or environmental pathogens without food fermentation relevance (n\u0026thinsp;\u0026asymp;\u0026thinsp;142), non-QS microbiology topics (n\u0026thinsp;\u0026asymp;\u0026thinsp;78), or cocoa agronomy without fermentation context (n\u0026thinsp;\u0026asymp;\u0026thinsp;33). The remaining 104 records were retrieved as full texts and assessed against the eligibility criteria. Full-text exclusions totalled 43 records: 20 lacked mechanistic QS data, 14 addressed non-food fermentation systems, and 9 were secondary reviews without primary QS evidence. Studies were appraised for methodological quality, clarity of QS detection techniques, ecological relevance to fermentation systems, and mechanistic depth, with emphasis on reports employing LC\u0026ndash;MS, GC\u0026ndash;MS, NMR, bioassays, genetic, and transcriptomic approaches [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e2.4 Data Extraction and Synthesis\u003c/h2\u003e \u003cp\u003eA total of 61 studies met all eligibility criteria and were included in the narrative synthesis. These comprised 28 studies characterising bacterial QS mechanisms, 18 addressing fungal QS and cross-kingdom signalling, 15 focused on cocoa fermentation microbiology with QS relevance, and 11 reporting analytical methods for QS molecule detection in complex food matrices. Several studies contributed to more than one category \u0026mdash; particularly the analytical methods papers, which also reported primary mechanistic data falling within the bacterial QS, fungal QS, or cocoa fermentation themes \u0026mdash; and were therefore counted in each applicable category; the category totals consequently sum to more than 61 unique included studies. Data extraction followed a structured template capturing: (i) QS molecules identified; (ii) microbial species involved; (iii) signalling pathways characterised; (iv) functional phenotypes regulated (e.g., biofilm formation, acid production, stress tolerance); (v) fermentation context; and (vi) analytical or genetic techniques employed. Where applicable, results from related studies were triangulated to resolve discrepancies or strengthen mechanistic interpretations. Given the mechanistic heterogeneity of included studies, no statistical meta-analysis was conducted; a narrative synthesis approach was applied throughout, with comparative analyses employed to contextualise cocoa pulp fermentation within broader food ecology literature.\u003c/p\u003e \u003c/div\u003e"},{"header":"3. Results and Discussion","content":"\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\n \u003ch2\u003e3.1 Bacterial Quorum Sensing in Food Fermentation Ecosystems\u003c/h2\u003e\n \u003cp\u003eBacterial QS constitutes a central regulatory mechanism coordinating collective behaviours through the production, release, and detection of extracellular signalling molecules. In food fermentation ecosystems, QS modulates metabolic coordination, stress adaptation, flavour precursor formation, and the spatial\u0026ndash;temporal structuring of microbial consortia [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e, \u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e]. The fundamental architecture spans AHL circuits in Gram-negative bacteria and peptide-mediated systems in Gram-positive species, enabling microbial communities to synchronise gene expression in response to population density or environmental cues [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. Understanding the mechanistic basis of these systems \u0026mdash; and the specific phenotypes they regulate \u0026mdash; is the prerequisite for applying QS knowledge to fermentation control in cocoa pulp wine.\u003c/p\u003e\n \u003cdiv id=\"Sec9\" class=\"Section3\"\u003e\n \u003ch2\u003e3.1.1 AHL-Mediated Signalling in Gram-Negative Fermentation Bacteria\u003c/h2\u003e\n \u003cp\u003eGram-negative bacteria employ AHLs as primary QS autoinducers, synthesised by LuxI-type enzymes and detected by cognate LuxR-type receptors, forming a transcriptional activation architecture whose discovery in Vibrio fischeri established the foundational QS paradigm [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e, \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e, \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e, \u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e59\u003c/span\u003e]. The structural diversity of AHL acyl chains \u0026mdash; ranging from C4 to C18 with variable 3-oxo and 3-hydroxy substituents \u0026mdash; confers inter-strain and inter-species signalling specificity [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e]. In food systems, AHL-mediated QS has been demonstrated in spoilage-associated Pseudomonas spp. [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e], in Erwinia spp. during plant material fermentation [\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e], and in acetic acid bacteria (AAB) of the genus Gluconacetobacter, which produce AHLs that regulate cooperative metabolism and oxidative activity [\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e]. These signalling interactions govern exopolysaccharide production, motility, enzyme secretion, and biofilm formation \u0026mdash; phenotypes directly relevant to AAB behaviour during the oxidative phase of cocoa pulp fermentation, where acetic acid production must be precisely modulated to prevent over-acidification of the wine matrix. A limitation of current evidence is that most AHL characterisation in AAB has been conducted in laboratory monocultures rather than in the competitive, polyphenol-containing, low-pH environments characteristic of cocoa pulp; the degree to which AHL signalling is quenched or preserved under these conditions remains to be established experimentally.\u003c/p\u003e\n \u003c/div\u003e\n \u003cdiv id=\"Sec10\" class=\"Section3\"\u003e\n \u003ch2\u003e3.1.2 AI-2 Signalling in Lactic Acid Bacteria\u003c/h2\u003e\n \u003cp\u003eThe LuxS/AI-2 system represents the most broadly distributed QS modality among LAB and the most directly evidenced in cocoa-associated microorganisms. AI-2 is synthesised via the LuxS enzyme and functions as a universal interspecies signal capable of mediating cross-species communication between Gram-positive and Gram-negative bacteria [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e, \u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e58\u003c/span\u003e]. In LAB, AI-2 activity is functionally linked to acid tolerance, biofilm maturation, bacteriocin synthesis, and competitive colonisation of fermentation substrates [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e, \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e, \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e]. The direct relevance to cocoa pulp wine is anchored by two key molecular findings: luxS gene homologs have been identified in cocoa-associated Lactiplantibacillus plantarum and Limosilactobacillus fermentum strains [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e], and a frameshift mutation in luxS was documented in cocoa-derived L. fermentum 87 [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e] \u0026mdash; demonstrating that QS pathway variation at the strain level may explain observed differences in fermentation performance between genotypically similar LAB isolates. Furthermore, luxS expression is upregulated in Lactobacillus spp. under acid stress [\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e], suggesting that AI-2 signalling functions as a stress-coordination mechanism during the pH transition phase of cocoa pulp fermentation. Although the universality of AI-2 has been questioned \u0026mdash; some bacterial genera lack functional AI-2 receptor systems [\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e] \u0026mdash; evidence from fermented food ecosystems consistently supports AI-2 as an ecologically active signal in multispecies LAB consortia. These observations collectively support the hypothesis that AI-2-mediated LAB communication actively structures the timing and extent of the acidification phase \u0026mdash; a hypothesis that has not yet been tested through direct QS molecule quantification in cocoa pulp wine matrices.\u003c/p\u003e\n \u003c/div\u003e\n \u003cdiv id=\"Sec11\" class=\"Section3\"\u003e\n \u003ch2\u003e3.1.3 Peptide-Based QS in Gram-Positive Bacteria\u003c/h2\u003e\n \u003cp\u003eGram-positive LAB regulate community behaviours through auto-inducing peptides (AIPs) acting via two-component signal transduction systems, governing bacteriocin production, proteolysis, adhesion, and surface colonisation [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e, \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e, \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e]. Nisin production by Lactococcus lactis, plantaricin regulation in Lactiplantibacillus plantarum, and Agr-type peptide signalling in staphylococci illustrate the functional breadth of peptide QS across food environments [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e, \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e, \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e, \u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e57\u003c/span\u003e]. The competitive relevance of peptide QS to cocoa pulp fermentation is substantial: in a substrate populated simultaneously by multiple LAB strains, yeasts, and AAB, bacteriocin-mediated competitive exclusion \u0026mdash; regulated by peptide QS \u0026mdash; may determine which LAB lineages dominate the mid-fermentation window and thereby dictate the organic acid balance and flavour precursor profile of the resulting wine. Specifically, nisin and plantaricin production can suppress non-LAB competitors and steer succession toward homofermentative or heterofermentative outcomes with direct consequences for the lactic/acetic acid ratio and overall wine stability. The challenge is that most peptide QS studies in LAB have been conducted in dairy or vegetable fermentation matrices; whether the highly acidic, polyphenol-rich, and ethanol-accumulating environment of cocoa pulp fermentation alters peptide signal stability or receptor sensitivity has not been investigated.\u003c/p\u003e\n \u003c/div\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\n \u003ch2\u003e3.2 Fungal Quorum Sensing and Cross-Kingdom Interactions\u003c/h2\u003e\n \u003cp\u003eBuilding on the bacterial QS evidence, a complete picture of communication dynamics in cocoa pulp wine fermentation requires integrating the fungal dimension. Yeasts dominate the early anaerobic phase and are the primary drivers of ethanol yield, CO₂ evolution, and initial flavour compound formation \u0026mdash; making their density-dependent signalling behaviours directly consequential for fermentation trajectory.\u003c/p\u003e\n \u003cdiv id=\"Sec13\" class=\"Section3\"\u003e\n \u003ch2\u003e3.2.1 Farnesol and Tyrosol as Fungal QS Molecules\u003c/h2\u003e\n \u003cp\u003eFarnesol, the first QS molecule identified in a eukaryote, accumulates extracellularly during Candida albicans growth and inhibits the yeast-to-hypha morphological transition at threshold concentrations, suppressing biofilm formation and modulating population structure [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e, \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e]. In food fermentation environments, farnesol\u0026apos;s impact on sterol biosynthesis and membrane integrity may alter volatile compound formation trajectories relevant to cocoa wine organoleptic profile [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]. Tyrosol, the functionally antagonistic second fungal QS molecule in C. albicans [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e], promotes germ tube formation and cooperative proliferation under sugar-rich, nitrogen-limited conditions \u0026mdash; conditions structurally analogous to early-phase cocoa pulp fermentation [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. The reciprocal regulation of yeast morphogenesis by these two signals establishes a density-sensitive switch whose net outcome depends on signal ratio and timing. In food yeasts beyond Candida, including Saccharomyces cerevisiae, non-pathogenic cocoa fermentation species such as Hanseniaspora uvarum and Pichia kudriavzevii produce aromatic alcohols including phenylethanol and tryptophol that regulate pseudohyphal growth and stress responses under nutrient restriction [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. A critical limitation of the current evidence base is that farnesol and tyrosol have been characterised almost exclusively in pathogenic Candida contexts; their production kinetics, effective concentrations, and receptor systems in these non-pathogenic cocoa fermentation yeasts are largely unknown.\u003c/p\u003e\n \u003c/div\u003e\n \u003cdiv id=\"Sec14\" class=\"Section3\"\u003e\n \u003ch2\u003e3.2.2 Cross-Kingdom Signalling and Fermentation Phase Transitions\u003c/h2\u003e\n \u003cp\u003eCross-kingdom QS interactions represent perhaps the most consequential and least characterised dimension of cocoa pulp wine fermentation. Farnesol suppresses AHL-mediated bacterial QS networks in Pseudomonas aeruginosa by interfering with receptor binding [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e], while bacterial AI-2 signals modulate yeast carbohydrate consumption rates and stress resilience [\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e]. In the sequential ecology of cocoa pulp fermentation \u0026mdash; yeast-dominated ethanol production, LAB-driven acidification, AAB-governed oxidative phase \u0026mdash; these reciprocal signals may accelerate or retard phase transitions in ways currently attributed solely to substrate depletion and pH change. Genome sequencing of cocoa-associated LAB strains has identified luxS gene homologs and QS-associated loci across fermentation-relevant species [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e], providing molecular evidence that the signalling machinery for cross-species communication is present in the cocoa ecosystem. Whether these pathways are transcriptionally active and functionally consequential during cocoa pulp wine fermentation remains the central unanswered question. Addressing it would fundamentally reframe our understanding of succession dynamics in this system and open the possibility of rational signal manipulation as a precision fermentation tool.\u003c/p\u003e\n \u003c/div\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec15\" class=\"Section2\"\u003e\n \u003ch2\u003e3.3 Synthesis of QS Evidence Across Fermentation Systems\u003c/h2\u003e\n \u003cp\u003eTable 1 summarises the principal QS molecules, organisms, regulated phenotypes, and analytical detection methods identified across the 61 studies included in this review, with explicit notation of evidence available from cocoa-relevant fermentation contexts.\u003c/p\u003e\n \u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"602\"\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 93px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eQS molecule\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eOrganism class\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 133px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eKey regulated phenotypes\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 107px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eDetection method\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 155px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCocoa/pulp evidence?\u0026dagger;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 93px;\"\u003e\n \u003cp\u003eAHLs (C4\u0026ndash;C18)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003eGram-negative bacteria (AAB, Pseudomonas, Erwinia)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 133px;\"\u003e\n \u003cp\u003eBiofilm, enzyme secretion, oxidative metabolism\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 107px;\"\u003e\n \u003cp\u003eLC\u0026ndash;MS/MS, MRM, bioassay\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 155px;\"\u003e\n \u003cp\u003eIndirect (AAB genomics)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 93px;\"\u003e\n \u003cp\u003eAI-2 (DPD)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003eLAB, Gram-negative bacteria\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 133px;\"\u003e\n \u003cp\u003eAcid tolerance, bacteriocin, biofilm, stress response\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 107px;\"\u003e\n \u003cp\u003eLC\u0026ndash;MS/MS, V. harveyi bioassay\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 155px;\"\u003e\n \u003cp\u003eDirect (luxS in cocoa LAB) [20, 21]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 93px;\"\u003e\n \u003cp\u003eAIPs (peptides)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003eGram-positive LAB\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 133px;\"\u003e\n \u003cp\u003eBacteriocin production, protease, adhesion\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 107px;\"\u003e\n \u003cp\u003eHPLC, bioassay, genetics\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 155px;\"\u003e\n \u003cp\u003eInferred (bacteriocin-mediated LAB competition)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 93px;\"\u003e\n \u003cp\u003eFarnesol\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003eCandida spp., yeasts\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 133px;\"\u003e\n \u003cp\u003eMorphogenesis, biofilm, sterol biosynthesis\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 107px;\"\u003e\n \u003cp\u003eLC\u0026ndash;MS/MS, HPLC-fluorescence\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 155px;\"\u003e\n \u003cp\u003eNot yet detected in cocoa pulp\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 93px;\"\u003e\n \u003cp\u003eTyrosol\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003eCandida spp., S. cerevisiae\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 133px;\"\u003e\n \u003cp\u003eGerm tube formation, growth promotion\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 107px;\"\u003e\n \u003cp\u003eHPLC, LC\u0026ndash;MS/MS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 155px;\"\u003e\n \u003cp\u003eNot yet detected in cocoa pulp\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 93px;\"\u003e\n \u003cp\u003ePhenylethanol / tryptophol\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003eS. cerevisiae\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 133px;\"\u003e\n \u003cp\u003ePseudohyphal growth, stress tolerance\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 107px;\"\u003e\n \u003cp\u003eGC\u0026ndash;MS, HPLC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 155px;\"\u003e\n \u003cp\u003eIndirectly relevant (cocoa yeasts)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003cdiv class=\"gridtable\"\u003e\n \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003cbr\u003e\u003c/div\u003e\n \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003cstrong\u003e\u0026dagger; Evidence strength categories\u003c/strong\u003e\u003c/div\u003e\n \u003c/div\u003e\n \u003cp\u003e\u003cstrong\u003eDirect\u003c/strong\u003e\u0026thinsp;\u003cem\u003e=\u0026thinsp;detected/quantified in cocoa fermentation contexts;\u003c/em\u003e \u003cstrong\u003eIndirect\u003c/strong\u003e\u0026thinsp;\u003cem\u003e=\u0026thinsp;inferred from genomics or closely analogous fermentation systems;\u003c/em\u003e \u003cstrong\u003eInferred\u003c/strong\u003e\u0026thinsp;\u003cem\u003e=\u0026thinsp;mechanistically plausible based on organism presence but not experimentally tested.\u003c/em\u003e\u003c/p\u003e\n \u003cp\u003eThis synthesis reveals a clear asymmetry in the evidence base. Bacterial QS \u0026mdash; particularly AI-2 in LAB \u0026mdash; is the best-evidenced pathway in cocoa-relevant contexts, with direct molecular data from cocoa-associated strains [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. AHL-mediated signalling in AAB has genomic and ecological plausibility but lacks in situ detection data from cocoa pulp matrices. Fungal QS molecules (farnesol, tyrosol) have extensive mechanistic characterisation in Candida biology but zero direct detection in any cocoa fermentation context. This asymmetry defines both the current state of knowledge and the specific experimental priorities for the field.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec16\" class=\"Section2\"\u003e\n \u003ch2\u003e3.4 Established Biochemical Pathway Frameworks in Cocoa Fermentation and the Missing Regulatory Layer\u003c/h2\u003e\n \u003cp\u003eThe evidence synthesised in Sections \u003cspan refid=\"Sec8\" class=\"InternalRef\"\u003e3.1\u003c/span\u003e\u0026ndash;\u003cspan refid=\"Sec15\" class=\"InternalRef\"\u003e3.3\u003c/span\u003e establishes QS as a mechanistically credible regulatory system in cocoa pulp wine fermentation. However, QS does not operate in isolation \u0026mdash; it sits within, and must be understood in relation to, the broader biochemical pathway frameworks that already describe how carbon, energy, and metabolites flow through this fermentation ecosystem. Four major frameworks currently govern scientific understanding of cocoa fermentation biochemistry. Each is rigorously supported by high-quality experimental evidence. Each also shares a fundamental limitation: none incorporates a microbial communication layer. Identifying precisely where QS fits within \u0026mdash; and extends \u0026mdash; these existing frameworks is the conceptual contribution of this section.\u003c/p\u003e\n \u003cdiv id=\"Sec17\" class=\"Section3\"\u003e\n \u003ch2\u003e3.4.1 The Three-Phase Succession Model: The Dominant Paradigm\u003c/h2\u003e\n \u003cp\u003eThe foundational framework for cocoa fermentation biochemistry is the three-phase microbial succession model, established through decades of culture-dependent and culture-independent community profiling by De Vuyst, Weckx, Schwan, Wheals, and their collaborators [\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e, \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e, \u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e65\u003c/span\u003e]. In this model, fermentation proceeds through three overlapping but distinguishable phases: an anaerobic yeast-dominant phase (0\u0026ndash;48 h) in which glucose and fructose are converted to ethanol and CO2; a microaerophilic LAB-dominant phase (24\u0026ndash;72 h) in which residual sugars and citrate are converted to lactic acid, acetic acid, mannitol, and diacetyl; and an aerobic AAB-dominant phase (48\u0026ndash;120\u0026thinsp;+\u0026thinsp;h) in which ethanol is oxidised to acetic acid through the sequential action of pyrroquinoline quinone-dependent alcohol dehydrogenase and acetaldehyde dehydrogenase [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. This succession model has been reproduced across fermentation systems in Ghana, Brazil, Ecuador, Malaysia, and Ivory Coast, and forms the basis of the starter culture development programme led by De Vuyst and colleagues at Vrije Universiteit Brussel \u0026mdash; the most comprehensive and authoritative body of experimental work in the field [\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e].\u003c/p\u003e\n \u003cp\u003eThe succession model is descriptively powerful. It accurately predicts the broad temporal ordering of microbial dominance and the major metabolite trajectories. Its limitation, however, is explanatory rather than descriptive: it attributes phase transitions to substrate depletion and oxygen availability \u0026mdash; passive, physicochemical triggers \u0026mdash; rather than to active, cell-density-dependent coordination. The model tells us what transitions occur and roughly when; it does not explain how the microbial community collectively decides when to transition or how individual species coordinate their metabolic outputs across guild boundaries. This explanatory gap is precisely the space that QS occupies.\u003c/p\u003e\n \u003c/div\u003e\n \u003cdiv id=\"Sec18\" class=\"Section3\"\u003e\n \u003ch2\u003e3.4.2 Carbon Flux and Fluxome Models: Metabolic Precision Without Communication\u003c/h2\u003e\n \u003cp\u003eA second and more mechanistically detailed class of frameworks uses metabolic flux analysis to quantify how carbon moves through specific enzymatic pathways within individual species and mixed communities under cocoa pulp simulation conditions. The core fluxome work of Adler, De Vuyst, and colleagues used 13C isotope labelling to map carbon flux through the Embden-Meyerhof-Parnas (EMP) pathway, the phosphoketolase (PPK) pathway, and citrate metabolism in LAB strains isolated from Ghanaian cocoa fermentations [\u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e62\u003c/span\u003e]. The complementary AAB fluxome analysis revealed that Acetobacter pasteurianus operates primarily through the pentose phosphate pathway (PPP) and gluconeogenesis rather than glycolysis, with lactate functioning not only as an energy source but as a carbon precursor for PPP-derived reducing equivalents [\u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e62\u003c/span\u003e, \u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e67\u003c/span\u003e]. Together, these fluxome models revealed that the metabolic interdependence of LAB and AAB \u0026mdash; LAB supplying both ethanol and lactate, AAB requiring both for optimal acetate production \u0026mdash; is more tightly coupled than the succession model implies.\u003c/p\u003e\n \u003cp\u003eThe fluxome frameworks are quantitatively precise at the single-species and pairwise level but face a fundamental limitation in mixed-community contexts: 13C-based metabolic flux analysis cannot easily resolve individual species contributions in complex consortia, and it captures only instantaneous metabolic states rather than the dynamic regulatory transitions between states [\u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e62\u003c/span\u003e]. Critically, the fluxome models contain no mechanism by which a cell knows what its neighbours are doing. A yeast cell producing ethanol has no representation in the fluxome model of whether the LAB population has reached a density at which AI-2 signalling is active or whether the AAB population has crossed the AHL quorum threshold. The fluxome describes the metabolic output of communication events; it does not describe the communication events themselves.\u003c/p\u003e\n \u003c/div\u003e\n \u003cdiv id=\"Sec19\" class=\"Section3\"\u003e\n \u003ch2\u003e3.4.3 Kinetic and Mathematical Models: Predicting Without Sensing\u003c/h2\u003e\n \u003cp\u003eA third framework class encompasses the mathematical and kinetic models of cocoa fermentation developed by Osborne, Morales-Contreras, and others, formulated as systems of coupled ordinary differential equations describing the temporal evolution of metabolite concentrations \u0026mdash; glucose, fructose, ethanol, lactic acid, acetic acid \u0026mdash; and microbial population sizes [\u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e63\u003c/span\u003e, \u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e64\u003c/span\u003e]. These models have been trained and validated on fermentation time series data from multiple producing regions and are capable of quantitatively reproducing microbial succession and metabolite kinetics under defined conditions. A Bayesian parameter estimation framework has further enabled reverse-engineering of environmental conditions from model parameters, opening an avenue toward condition discrimination between fermentation protocols [\u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e63\u003c/span\u003e].\u003c/p\u003e\n \u003cp\u003eThe kinetic models are powerful predictive tools within their parametric domain. However, they share a structural assumption that defines their scope and their limitation: microbial growth rates and metabolite conversion kinetics are modelled as functions of substrate concentrations, pH, temperature, and oxygen availability \u0026mdash; all physicochemical state variables. The models contain no biological communication terms. Phase transitions are triggered by substrate thresholds and oxygen flux, not by quorum threshold crossings. As a consequence, these models cannot distinguish between a fermentation that transitions from yeast to LAB dominance because glucose is depleted and one that transitions because an AI-2 quorum threshold has been crossed and LAB have coordinately upregulated acid stress tolerance genes. Both produce the same metabolite trajectory in the model; only one reflects the actual biological mechanism. Adding QS signal concentration terms as state variables to these kinetic frameworks would represent a fundamental architectural advance \u0026mdash; one that this review provides the mechanistic basis for.\u003c/p\u003e\n \u003c/div\u003e\n \u003cdiv id=\"Sec20\" class=\"Section3\"\u003e\n \u003ch2\u003e3.4.4 The Shetty Proline-Linked Pentose Phosphate Pathway Framework: A Substrate-Side Redox Model\u003c/h2\u003e\n \u003cp\u003eA fourth and conceptually distinct framework is the proline-linked pentose phosphate pathway (PLPPP) model proposed by Shetty and Wahlqvist, developed principally in the context of phenolic phytochemical biosynthesis and microbial biotransformation of plant-derived substrates [\u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e60\u003c/span\u003e, \u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e61\u003c/span\u003e]. This model proposes that proline cycling \u0026mdash; the reversible interconversion of proline and pyrroline-5-carboxylate mediated by proline dehydrogenase \u0026mdash; acts as a cellular redox valve that stimulates flux through the oxidative pentose phosphate pathway (ox-PPP). Increased PPP flux generates NADPH and erythrose-4-phosphate; erythrose-4-phosphate feeds the shikimate pathway, which is the entry point to the phenylpropanoid pathway responsible for phenolic compound biosynthesis. The model predicts that microbial or environmental stressors that increase proline cycling will simultaneously increase PPP activity, phenolic mobilisation, and antioxidant enzyme response \u0026mdash; a coupled redox-phenolic network that has been validated in LAB fermentation of plant matrices including barley, legumes, sweet potatoes, and fruit substrates.\u003c/p\u003e\n \u003cp\u003eThe PLPPP framework is directly relevant to cocoa pulp wine fermentation for two reasons. First, cocoa pulp is a polyphenol-rich substrate \u0026mdash; containing catechins, procyanidins, and anthocyanins at concentrations of 6\u0026ndash;8% w/v \u0026mdash; meaning that the substrate-side redox dynamics described by Shetty\u0026rsquo;s model are likely active throughout fermentation as microbial species interact with and biotransform the cocoa phenolic matrix. Second, and most importantly for this review, the same polyphenolic compounds that the PLPPP model identifies as products of PPP-driven biosynthesis are also the primary quenchers of QS signals in the cocoa pulp matrix: anthocyanins and procyanidins promote lactonase-mimetic AHL hydrolysis, and catechins competitively inhibit AI-2 receptor binding [\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e]. This creates a bidirectional interface between Shetty\u0026rsquo;s substrate-side redox model and the QS communication framework that has not previously been described in cocoa fermentation literature.\u003c/p\u003e\n \u003c/div\u003e\n \u003cdiv id=\"Sec21\" class=\"Section3\"\u003e\n \u003ch2\u003e3.4.5 The Bidirectional Polyphenol\u0026ndash;QS\u0026ndash;Redox Interface: A Novel Conceptual Bridge\u003c/h2\u003e\n \u003cp\u003eThe juxtaposition of these four frameworks with the QS evidence synthesised in this review reveals a regulatory circuit that none of the existing models describes. The circuit operates as follows. During early fermentation, cocoa pulp polyphenols \u0026mdash; present at high initial concentrations and actively biotransformed by yeasts \u0026mdash; quench AHL signals through lactonase-mimetic hydrolysis and competitive receptor inhibition, reducing effective QS signal availability at the AAB quorum threshold [\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e]. This polyphenol-mediated quenching delays AAB community coordination, extending the yeast-dominant phase and protecting early ethanol accumulation from premature oxidation. As yeast metabolism and LAB acidification progressively degrade and biotransform the polyphenol pool \u0026mdash; reducing catechin and procyanidin concentrations through enzymatic oxidation and acid hydrolysis \u0026mdash; the polyphenol buffering capacity for QS quenching diminishes. AHL signals from AAB populations begin to accumulate above the quorum threshold, triggering coordinated upregulation of oxidative metabolism and biofilm formation at oxygen interfaces. Concurrently, AI-2 from LAB reaches signalling concentrations, coordinating the acidification phase. The sequential resolution of polyphenol quenching thus functions as a natural timing mechanism for QS-mediated phase transitions \u0026mdash; one that is entirely invisible to the succession model, the fluxome frameworks, and the kinetic ODE models.\u003c/p\u003e\n \u003cp\u003eThis circuit has a further dimension in the PLPPP context. Shetty\u0026rsquo;s model predicts that microbial stress \u0026mdash; including acid stress, ethanol toxicity, and oxidative stress during fermentation \u0026mdash; increases proline cycling and PPP flux in fermenting microorganisms, driving phenolic biotransformation and antioxidant enzyme response. If increased PPP flux simultaneously increases production of phenolic compounds from the residual substrate pool, this would provide positive feedback on QS quenching during stress phases \u0026mdash; a mechanism by which the fermentation community may collectively dampen inter-kingdom communication signals during periods of maximum metabolic stress, only releasing QS coordination once stress is partially resolved by substrate depletion. Whether this feedback is biologically significant in cocoa pulp wine fermentation is an open empirical question; but its theoretical coherence \u0026mdash; grounded in established mechanisms from both the PLPPP and QS literatures \u0026mdash; makes it a testable hypothesis that can be addressed in controlled fermentation trials through paired LC\u0026ndash;MS/MS QS signal quantification and polyphenol profiling at defined fermentation time points.\u003c/p\u003e\n \u003c/div\u003e\n \u003cdiv id=\"Sec22\" class=\"Section3\"\u003e\n \u003ch2\u003e3.4.6 Constructing the QS Pathway Framework for Cocoa Pulp Wine Fermentation\u003c/h2\u003e\n \u003cp\u003eDrawing on the evidence synthesised in Sections \u003cspan refid=\"Sec8\" class=\"InternalRef\"\u003e3.1\u003c/span\u003e\u0026ndash;\u003cspan refid=\"Sec15\" class=\"InternalRef\"\u003e3.3\u003c/span\u003e and the framework analysis above, a conceptual QS pathway framework can now be constructed for this specific fermentation ecosystem. The framework has four components that operate simultaneously across the three fermentation phases (Fig. \u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e\n \u003cp\u003eThe first component is the polyphenol quenching envelope \u0026mdash; a time-varying suppression function on QS signal availability determined by the concentration and composition of cocoa pulp polyphenols at each fermentation stage. This envelope is highest at fermentation onset and decreases progressively as polyphenols are biotransformed, determining the temporal window within which QS signals can accumulate to threshold concentrations. It interfaces directly with Shetty\u0026rsquo;s PLPPP model, as PPP-driven phenolic biotransformation rates in LAB and yeasts determine the rate at which the quenching envelope contracts.\u003c/p\u003e\n \u003cp\u003eThe second component is the intra-kingdom QS layer \u0026mdash; the density-dependent signalling circuits within each microbial guild. Yeast farnesol and tyrosol circuits operate within the yeast population to regulate population density, morphogenesis, and ester biosynthesis during Phase I. AI-2 and peptide AIP circuits operate within the LAB population to coordinate acidification rate, bacteriocin production, and acid stress tolerance during Phase II. AHL circuits operate within the AAB population to gate oxidative metabolism during Phase III. Each of these intra-kingdom circuits is directly regulated by the polyphenol quenching envelope, with the degree of regulation depending on the structural specificity of the interaction between the signal molecule and the polyphenolic compound.\u003c/p\u003e\n \u003cp\u003eThe third component is the cross-kingdom QS interface \u0026mdash; the inter-guild signalling interactions described in Section \u003cspan refid=\"Sec14\" class=\"InternalRef\"\u003e3.2.2\u003c/span\u003e. Yeast-derived farnesol suppresses premature AAB biofilm formation, functioning as a cross-kingdom gating signal for the Phase II\u0026ndash;III transition. AI-2 from LAB may modulate yeast carbohydrate assimilation rates, influencing ethanol yield during Phase I. These cross-kingdom signals add regulatory bandwidth beyond what any single-guild intra-kingdom circuit can achieve, enabling the community to coordinate the timing of phase transitions as an integrated ecological system rather than as independent populations responding to common environmental cues.\u003c/p\u003e\n \u003cp\u003eThe fourth component is the metabolite output layer \u0026mdash; the sensory-relevant metabolites whose production rates are directly determined by the state of the QS regulatory layers above. Ester yield and aromatic character are downstream of the yeast farnesol/tyrosol circuit; acidity profile and mouthfeel are downstream of the LAB AI-2 and peptide AIP circuits; volatile acidity and oxidative aroma character are downstream of the AAB AHL circuit and cross-kingdom farnesol gating. The mechanistic linkages in this layer were detailed in Section \u003cspan refid=\"Sec25\" class=\"InternalRef\"\u003e3.7\u003c/span\u003e [Table \u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e] and provide the measurable endpoints against which the QS pathway framework can be experimentally validated in controlled fermentation trials.\u003c/p\u003e\n \u003cp\u003eFigure 2 presents this QS pathway framework conceptually, showing the four components, their interactions, and their relationship to the phase structure of the fermentation. The framework is explicitly positioned as an extension of, not a replacement for, the succession model, fluxome, and kinetic ODE frameworks. It adds the regulatory communication layer that those frameworks lack, providing the mechanistic basis for predicting why phase transitions occur at specific times under specific conditions \u0026mdash; a question that substrate depletion and oxygen availability alone cannot answer. In this sense, the QS pathway framework proposed here constitutes the missing fourth dimension of cocoa pulp fermentation modelling: alongside the ecological dimension (succession), the biochemical dimension (carbon flux), and the kinetic dimension (population dynamics), it provides the communication dimension that governs the timing, coordination, and quality consequences of the transitions between all three.\u003c/p\u003e\n \u003c/div\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec23\" class=\"Section2\"\u003e\n \u003ch2\u003e3.5 Analytical Detection of QS Molecules in Complex Food Matrices\u003c/h2\u003e\n \u003cp\u003eThe asymmetry identified in Table \u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e is partly a reflection of analytical accessibility rather than mechanistic absence. Cocoa pulp presents a chemically hostile matrix for QS detection: polyphenolic compounds \u0026mdash; anthocyanins, catechins, and procyanidins \u0026mdash; can quench AHL activity through lactonase-mimetic hydrolysis (enzymatic-like ring-opening of the AHL lactone moiety) and competitive receptor inhibition, potentially masking signals or producing ion suppression during mass spectrometric analysis [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e, \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e, \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e]. The acidic pH of fermenting cocoa pulp (pH 3.5\u0026ndash;5.0) additionally promotes AHL lactone ring hydrolysis, necessitating matrix-matched stabilisation protocols during sample preparation. These challenges explain in part why QS detection in cocoa systems has so far been limited to genomic inference rather than direct measurement.\u003c/p\u003e\n \u003cp\u003eLC\u0026ndash;MS/MS has emerged as the validated reference platform for multi-analyte QS profiling in complex biological matrices, offering the sensitivity and structural resolution required to simultaneously detect AHLs, AI-2 via DPD derivatisation, and fungal aromatic alcohols from a single sample aliquot [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e, \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e]. In analogous food systems where such analysis has been applied, the results have been informative: AI-2 activity detected in kimchi LAB populations correlated with bacteriocin-driven competitive shifts during succession [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]; AHL profiling in dairy matrices identified strain-specific signalling windows linked to biofilm formation and protease activity peaks [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]; and fungal aromatic alcohol monitoring in wine fermentations revealed density-dependent tyrosol accumulation patterns that preceded yeast morphological transitions [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. Each of these findings was only possible because the analytical method was validated against the relevant food matrix \u0026mdash; a step that has not yet been taken for cocoa pulp wine.\u003c/p\u003e\n \u003cp\u003eThe practical roadmap for establishing cocoa pulp wine QS analytics involves the following sequential steps:\u003c/p\u003e\u003cspan\u003e\n \u003cp\u003e1. Adapt existing LC\u0026ndash;MS/MS MRM workflows to cocoa pulp matrix-matched calibration standards with solid-phase extraction cleanup to address polyphenol interference.\u003c/p\u003e\n \u003c/span\u003e \u003cspan\u003e\n \u003cp\u003e2. Develop a DPD derivatisation protocol for AI-2 quantification in acidic pulp fermentates.\u003c/p\u003e\n \u003c/span\u003e \u003cspan\u003e\n \u003cp\u003e3. Include deuterated internal standards for AHLs and aromatic alcohols to correct for matrix-dependent ion suppression.\u003c/p\u003e\n \u003c/span\u003e \u003cspan\u003e\n \u003cp\u003e4. Sample at defined fermentation time points \u0026mdash; 0, 12, 24, 48, 72, 96, and 144 h \u0026mdash; aligned with known microbial succession windows to capture signal dynamics rather than single-point snapshots.\u003c/p\u003e\n \u003c/span\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec24\" class=\"Section2\"\u003e\n \u003ch2\u003e3.6 Implications for Precision Fermentation and Starter Culture Design\u003c/h2\u003e\n \u003cp\u003eThe evidence synthesised across Sections \u003cspan refid=\"Sec8\" class=\"InternalRef\"\u003e3.1\u003c/span\u003e\u0026ndash;\u003cspan refid=\"Sec23\" class=\"InternalRef\"\u003e3.5\u003c/span\u003e converges on a single practical proposition: QS signalling in cocoa pulp wine fermentation is a mechanistically active regulatory layer that, once characterised, could be deliberately engaged as a fermentation control tool. The strongest immediate application lies in starter culture design. Selecting LAB strains with characterised high AI-2 production would serve to accelerate and synchronise the acidification transition, reducing the window during which the fermentation is vulnerable to spoilage-associated community shifts. Co-inoculating yeast strains with defined aromatic alcohol production profiles would allow modulation of cross-kingdom interactions during the anaerobic phase. Monitoring AHL concentration as a process indicator of AAB community activation during the oxidative phase would provide a molecular trigger for timing aeration and temperature interventions. These applications are not speculative \u0026mdash; each has a direct precedent in other fermented beverage systems, including starter culture development in cocoa bean fermentation [\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e] and QS-informed LAB consortia design in dairy fermentations [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e].\u003c/p\u003e\n \u003cp\u003eA critical evidence gap, however, constrains translation from principle to practice: no study has yet demonstrated a causal link between a specific QS molecule, its concentration dynamics, and a measurable fermentation outcome in cocoa pulp wine. Table \u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e shows that the most relevant QS pathways are supported by indirect or inferred evidence in cocoa-specific contexts. Closing this gap requires the experimental programme outlined in Section \u003cspan refid=\"Sec23\" class=\"InternalRef\"\u003e3.5\u003c/span\u003e \u0026mdash; in situ QS signal quantification throughout cocoa pulp wine fermentation \u0026mdash; as the essential first step. Until that data exists, QS-informed starter culture design for cocoa wine must be treated as an evidence-based hypothesis rather than an established protocol. The research priorities are clear: in situ detection, functional bioassay validation, transcriptomic characterisation of QS-regulated gene expression in cocoa-associated strains, and integration of QS time-series data with metabolomic and sensory endpoints to establish the empirical linkages that will enable translation to practice.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec25\" class=\"Section2\"\u003e\n \u003ch2\u003e3.7 Quorum Sensing as a Mechanistic Determinant of Sensory Quality in Cocoa Pulp Wine\u003c/h2\u003e\n \u003cp\u003eThe ultimate purpose of understanding QS in cocoa pulp wine fermentation is not mechanistic completeness per se, but the ability to engineer a beverage with defined, reproducible sensory attributes \u0026mdash; flavour, aroma, acidity, and mouthfeel \u0026mdash; from a substrate historically characterised by quality variability. This connection between QS signalling and sensory outcome is not merely inferential; it is mechanistically traceable through the metabolic pathways that each QS modality controls at each fermentation phase. Establishing these linkages is the scientific foundation upon which QS-guided starter culture design and predictive fermentation modelling must ultimately rest.\u003c/p\u003e\n \u003cp\u003eDuring the yeast-dominant phase, farnesol and tyrosol regulate population density, morphological switching, and the balance between ethanol production and ester biosynthesis. At physiologically relevant concentrations, farnesol suppresses filamentation and redirects yeast metabolic flux toward ester-producing pathways, favouring the accumulation of ethyl acetate, isoamyl acetate, and 2-phenylethyl acetate \u0026mdash; volatile esters that contribute the fruity, floral aromatic character considered desirable in cocoa pulp wine [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. Tyrosol, by promoting cooperative proliferation under nitrogen-limited conditions typical of late-stage cocoa pulp, sustains yeast biomass and prolongs ester synthesis beyond what would occur under unregulated density decline [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. The ratio of farnesol to tyrosol therefore functions as a molecular dial governing the aromatic foundation of the wine: high farnesol relative to tyrosol during early fermentation promotes ester accumulation and fruity character, while imbalance in either direction risks either premature yeast senescence (loss of aroma complexity) or unchecked filamentation (off-flavour formation). Because these signals are density-dependent and substrate-sensitive, their dynamics in cocoa pulp will differ from the Candida pathogenic contexts in which they have been characterised, underscoring the need for direct measurement in this matrix.\u003c/p\u003e\n \u003cp\u003eDuring the LAB-dominant acidification phase, AI-2-mediated coordination governs the rate and trajectory of lactic acid accumulation, with direct consequences for the acidity and mouthfeel of the finished wine. AI-2 upregulation under acid stress [\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e] suggests that the signal functions as a community-level stress integrator: as pH falls and lactic acid accumulates, AI-2 production increases, reinforcing coordinated acid tolerance responses and sustaining LAB metabolic activity into the lower pH range. The practical sensory consequence is that QS-competent LAB strains \u0026mdash; those with intact luxS and functional AI-2 signalling \u0026mdash; are likely to drive more complete, controlled acidification than QS-deficient strains, producing wines with cleaner acidity profiles and lower residual sugar. The frameshift mutation in luxS documented in cocoa-derived L. fermentum 87 [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e] implies that this strain may exhibit less coordinated acidification behaviour, contributing to the batch-to-batch pH variability observed in spontaneous cocoa pulp fermentations. Bacteriocin production, also regulated through peptide QS circuits [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e], further shapes mouthfeel and microbial safety by suppressing off-flavour-producing contaminants \u0026mdash; a function with direct organoleptic implications not yet characterised in cocoa pulp wine.\u003c/p\u003e\n \u003cp\u003eThe AAB-dominated oxidative phase presents the most direct and consequential QS-sensory linkage in cocoa pulp wine production. AHL accumulation above the quorum threshold in Acetobacter and Gluconacetobacter populations coordinates the upregulation of alcohol dehydrogenase and aldehyde dehydrogenase expression, driving the oxidation of ethanol to acetaldehyde and subsequently to acetic acid [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e, \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e]. The concentration and timing of this transition determine volatile acidity \u0026mdash; the single most important sensory defect threshold in wine-style fermented beverages. Controlled, QS-gated AAB oxidation produces moderate acetic acid levels that contribute complexity and preservation character to cocoa wine; uncontrolled or premature AHL-triggered oxidation produces vinegar-like harshness that renders the product unacceptable. Critically, farnesol produced by the residual yeast population during this phase may suppress premature AAB biofilm formation at oxygen interfaces [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e], acting as a cross-kingdom brake on oxidative metabolism. This yeast-derived QS signal therefore functions as a natural gating mechanism for acetic acid accumulation \u0026mdash; a regulatory interaction that, if confirmed through direct detection in cocoa pulp wine, would explain much of the inter-batch volatile acidity variability currently attributed to uncontrolled aeration.\u003c/p\u003e\n \u003cp\u003eTable 2 summarises these direct mechanistic linkages between the major QS signals, the metabolic pathways they regulate, and the sensory attributes they govern across the three phases of cocoa pulp wine fermentation.\u003c/p\u003e\n \u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"602\"\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eQS signal\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 93px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePhase\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 133px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMetabolic pathway regulated\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 133px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSensory attribute governed\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 155px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eFailure mode if QS disrupted\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003eFarnesol\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 93px;\"\u003e\n \u003cp\u003ePhase I (Yeast-dominant)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 133px;\"\u003e\n \u003cp\u003eSuppresses filamentation; redirects flux to ester biosynthesis (ethyl acetate, isoamyl acetate, 2-phenylethyl acetate)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 133px;\"\u003e\n \u003cp\u003eFruity, floral aroma; ester-alcohol balance\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 155px;\"\u003e\n \u003cp\u003eFlat or solvent-like aroma; premature yeast senescence; reduced ester formation\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003eTyrosol\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 93px;\"\u003e\n \u003cp\u003ePhase I (Yeast-dominant)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 133px;\"\u003e\n \u003cp\u003ePromotes cooperative proliferation; sustains ethanol and secondary metabolite production under N-limitation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 133px;\"\u003e\n \u003cp\u003eEthanol yield; body and mouthfeel; sustained aroma synthesis\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 155px;\"\u003e\n \u003cp\u003eThin mouthfeel; incomplete sugar conversion; loss of aromatic complexity\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003eAI-2 (LuxS/AI-2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 93px;\"\u003e\n \u003cp\u003ePhase II (LAB-dominant)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 133px;\"\u003e\n \u003cp\u003eCoordinates acidification rate, acid stress tolerance, bacteriocin synthesis, and competitive exclusion of spoilage organisms\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 133px;\"\u003e\n \u003cp\u003ePerceived acidity; clean vs. sharp sourness; mouthfeel smoothness; microbial safety\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 155px;\"\u003e\n \u003cp\u003eUncontrolled acidification; variable pH; spoilage proliferation; harsh or inconsistent tartness\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003eAIPs (peptide QS)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 93px;\"\u003e\n \u003cp\u003ePhase II (LAB-dominant)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 133px;\"\u003e\n \u003cp\u003eNisin/plantaricin-mediated spoilage exclusion; protease activation; LAB succession structuring\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 133px;\"\u003e\n \u003cp\u003eFlavour cleanness; absence of biogenic amines; structural mouthfeel from proteolytic activity\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 155px;\"\u003e\n \u003cp\u003eOff-flavour from spoilage; biogenic amine accumulation; thin or unstructured mouthfeel\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003eAHLs (C4-C18)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 93px;\"\u003e\n \u003cp\u003ePhase III (AAB-oxidative)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 133px;\"\u003e\n \u003cp\u003eGates alcohol/aldehyde dehydrogenase expression; coordinates O2-interface biofilm; regulates ethanol-to-acetic acid conversion rate\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 133px;\"\u003e\n \u003cp\u003eVolatile acidity level; aroma complexity (acetaldehyde, acetate esters); oxidative character\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 155px;\"\u003e\n \u003cp\u003eVinegar-like harshness from over-acidification; or flat aroma from insufficient oxidative contribution\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003eFarnesol (cross-kingdom)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 93px;\"\u003e\n \u003cp\u003eYeast to AAB transition\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 133px;\"\u003e\n \u003cp\u003eSuppresses premature AAB biofilm at O2 interfaces; gates onset of oxidative metabolism\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 133px;\"\u003e\n \u003cp\u003eTiming and degree of volatile acidity; phase transition sharpness\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 155px;\"\u003e\n \u003cp\u003ePremature oxidation; acetic acid overshoot; loss of fruity esters; vinegar character\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003cp\u003eTable \u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e. \u003cem\u003eMechanistic linkages between QS signals, regulated metabolic pathways, and sensory attributes in cocoa pulp wine fermentation across the three fermentation phases, with failure modes under QS disruption. Phase I\u0026thinsp;=\u0026thinsp;yeast-dominant alcoholic phase; Phase II\u0026thinsp;=\u0026thinsp;LAB-dominant acidification phase; Phase III\u0026thinsp;=\u0026thinsp;AAB-oxidative phase.\u003c/em\u003e\u003c/p\u003e\n \u003cp\u003eTaken together, these linkages establish that the sensory quality of cocoa pulp wine \u0026mdash; its fruity aromatic character, acidity profile, mouthfeel, and volatile balance \u0026mdash; is not simply the emergent result of substrate chemistry and temperature. It is, in substantial part, the phenotypic output of QS-governed metabolic decisions made by the microbial community at defined population density thresholds. This reframing has an important practical consequence: sensory consistency cannot be fully achieved by controlling physicochemical fermentation parameters alone. It requires understanding \u0026mdash; and ultimately controlling \u0026mdash; the chemical communication layer that drives the metabolic decisions underlying those parameters. The mechanistic linkages established in Table \u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e, grounded in the QS evidence synthesised across Sections \u003cspan refid=\"Sec8\" class=\"InternalRef\"\u003e3.1\u003c/span\u003e\u0026ndash;\u003cspan refid=\"Sec24\" class=\"InternalRef\"\u003e3.6\u003c/span\u003e and measurable through the analytical roadmap in Section \u003cspan refid=\"Sec23\" class=\"InternalRef\"\u003e3.5\u003c/span\u003e, provide the scientific foundation for that control. Their translation into QS-guided starter culture design and predictive fermentation modelling represents the direct next step in this research programme.\u003c/p\u003e\n\u003c/div\u003e"},{"header":"4. Conclusion","content":"\u003cp\u003eThis systematic review establishes quorum sensing as a mechanistically plausible and biotechnologically tractable regulatory layer in cocoa pulp wine fermentation. AHL-mediated signalling in acetic acid bacteria, LuxS/AI-2 circuits in lactic acid bacteria, and aromatic alcohol-based signalling in fermentation yeasts each represent functional QS modalities that are active in closely related food fermentation systems and encoded in cocoa-associated microbial genomes. Cross-kingdom interactions between these systems add a further dimension of regulatory complexity that has not yet been experimentally mapped in cocoa pulp wine contexts.\u003c/p\u003e \u003cp\u003eThe central gap identified by this review is not a lack of relevant QS biology but a lack of direct measurement in cocoa pulp matrices. Closing this gap requires the deployment of validated LC\u0026ndash;MS/MS analytical protocols adapted to the polyphenol-rich cocoa matrix, combined with targeted functional studies linking QS signal dynamics to fermentation phase transitions and metabolite trajectories. When such data become available, QS profiling is positioned to serve as both a scientific tool for understanding microbial ecology and a practical instrument for precision fermentation control.\u003c/p\u003e \u003cp\u003eFor cocoa-producing regions such as Ghana \u0026mdash; responsible for approximately 20% of global cocoa supply \u0026mdash; where fermentation quality is a determinant of both export value and smallholder livelihoods, the translation of QS-guided fermentation science into accessible starter culture and monitoring technologies represents a high-impact research frontier. Cocoa pulp wine, produced from a substrate historically discarded as waste, is positioned to become a model system for biodigital terroir: a scientifically optimised, traceably controlled, and culturally grounded fermented beverage product.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003eAAB — Acetic Acid Bacteria; AHL — Acyl-Homoserine Lactone; AI-2 — Autoinducer-2; AIP — Autoinducing Peptide; CCST — CSIR College of Science and Technology; DPD — 4,5-dihydroxy-2,3-pentanedione; GC — Gas Chromatography; HPLC — High-Performance Liquid Chromatography; LAB — Lactic Acid Bacteria; LC — Liquid Chromatography; LC-MS/MS — Liquid Chromatography–Tandem Mass Spectrometry; MRM — Multiple Reaction Monitoring; MS — Mass Spectrometry; ODE — Ordinary Differential Equation; PPP — Pentose Phosphate Pathway; PRISMA — Preferred Reporting Items for Systematic Reviews and Meta-Analyses; QS — Quorum Sensing.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eFunding:\u0026nbsp;\u003c/strong\u003eNo external funding was received for this work.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflict of Interest:\u0026nbsp;\u003c/strong\u003eThe authors declare no conflict of interest.\u003c/p\u003e\n\u003cp\u003eConsent for publication: Not applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor Contributions:\u0026nbsp;\u003c/strong\u003eConceptualization, Anthony Oppong Kyekyeku, Margaret Owusu, John Edem Kongor and Daniel Sitsofe Yabani; Methodology, Margaret Owusu, John Edem Kongor and Daniel Sitsofe Yabani; Validation, Margaret Owusu, John Edem Kongor and Daniel Sitsofe Yabani; Writing – Original Draft Preparation, Anthony Oppong Kyekyeku; Writing – Review \u0026amp; Editing, Anthony Oppong Kyekyeku, Margaret Owusu, John Edem Kongor and Daniel Sitsofe Yabani; Supervision, Margaret Owusu, John Edem Kongor and Daniel Sitsofe Yabani.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData Availability:\u0026nbsp;\u003c/strong\u003eNot applicable (review article; no primary datasets generated).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthical Approval:\u0026nbsp;\u003c/strong\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003eAcknowledgements: The authors acknowledge all who provided guidance and support during the development of the research programme underpinning this work.\u003c/p\u003e\n\u003cp\u003eUse of AI tools: The authors used Anthropic’s Claude AI assistant to support language polishing, structural review, and reference formatting during manuscript preparation. All AI-assisted outputs were critically reviewed, edited, and verified by the authors, who take full responsibility for the content and conclusions of this manuscript.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n \u003cli\u003eAlbuquerque, P., \u0026amp; Casadevall, A. (2012). Quorum sensing in fungi \u0026mdash; a review. Medical Mycology, 50(4), 337\u0026ndash;345. https://doi.org/10.3109/13693786.2011.652201\u003c/li\u003e\n \u003cli\u003eAlem, M.A., Oteef, M.D., Flowers, T.H., \u0026amp; Douglas, L.J. (2006). Production of tyrosol by Candida albicans biofilms and its role in quorum sensing and biofilm development. Eukaryotic Cell, 5, 1770\u0026ndash;1779. https://doi.org/10.1128/EC.00219-06\u003c/li\u003e\n \u003cli\u003eBassler, B.L., Wright, M., Showalter, R.E., \u0026amp; Silverman, M.R. (1993). Intercellular signalling in Vibrio harveyi: sequence and function of genes regulating expression of luminescence. 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Comprehensive profiling of N-acylhomoserine lactones produced by Yersinia pseudotuberculosis using liquid chromatography coupled to hybrid quadrupole\u0026ndash;linear ion trap mass spectrometry. Analytical and Bioanalytical Chemistry, 387, 497\u0026ndash;511. https://doi.org/10.1007/s00216-006-0710-0\u003c/li\u003e\n \u003cli\u003eNeiditch, M.B., Federle, M.J., Pompeani, A.J., Kelly, R.C., Swem, D.L., Jeffrey, P.D., \u0026amp; Bassler, B.L. (2006). Ligand-induced asymmetry in histidine sensor kinase complex regulates quorum sensing. Cell, 126(6), 1095\u0026ndash;1108.\u003c/li\u003e\n \u003cli\u003eNovick, R.P., \u0026amp; Geisinger, E. (2008). Quorum sensing in staphylococci. Annual Review of Genetics, 42, 541\u0026ndash;564.\u003c/li\u003e\n \u003cli\u003eNovick, R. P. (2003). 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Microbiological and Physicochemical Characterization of Small-Scale Cocoa Fermentations and Screening of Yeast and Bacterial Strains To Develop a Defined Starter Culture. \u003cem\u003eApplied and Environmental Microbiology\u003c/em\u003e, \u003cem\u003e78\u003c/em\u003e(15), 5395\u0026ndash;5405. https://doi.org/10.1128/AEM.01144-12\u003c/li\u003e\n \u003cli\u003eP\u0026eacute;rez, P. D., Weiss, J. T., \u0026amp; Hagen, S. J. (2011). Noise and crosstalk in two quorum-sensing inputs of Vibrio fischeri. \u003cem\u003eBMC Systems Biology\u003c/em\u003e, \u003cem\u003e5\u003c/em\u003e(1), Article 153. https://doi.org/10.1186/1752-0509-5-153\u003c/li\u003e\n \u003cli\u003ePereira, G.V.M., Magalh\u0026atilde;es, K.T., de Almeida, E.G., da Silva Coelho, I., \u0026amp; Schwan, R.F. (2013). Spontaneous cocoa bean fermentation carried out in a novel-design stainless steel tank: influence on microbial populations and physicochemical properties. International Journal of Food Microbiology, 161(2), 121\u0026ndash;133.\u003c/li\u003e\n \u003cli\u003eRezzonico, F., \u0026amp; Duffy, B. (2008). Lack of genomic evidence of AI-2 receptors suggests a non-quorum sensing role for luxS in most bacteria. \u003cem\u003eBMC Microbiology\u003c/em\u003e, \u003cem\u003e8\u003c/em\u003e(1), 154\u0026ndash;154. https://doi.org/10.1186/1471-2180-8-154\u003c/li\u003e\n \u003cli\u003eRutherford, S.T., \u0026amp; Bassler, B.L. (2012). Bacterial quorum sensing: its role in virulence and possibilities for its control. Cold Spring Harbor Perspectives in Medicine, 2(11), a012427.\u003c/li\u003e\n \u003cli\u003eDe Vuyst, L., \u0026amp; Weckx, S. (2016). The cocoa bean fermentation process: from ecosystem analysis to starter culture development. Journal of Applied Microbiology, 121(1), 5\u0026ndash;17. https://doi.org/10.1111/jam.13144\u003c/li\u003e\n \u003cli\u003eSchaefer, A.L., Val, D.L., Hanzelka, B.L., Cronan, J.E., \u0026amp; Greenberg, E.P. 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(2019). 3-Benzyl-hexahydro-pyrrolo [1, 2-a] pyrazine-1, 4-dione extracted from Exiguobacterium indicum showed anti-biofilm activity against Pseudomonas aeruginosa by attenuating quorum sensing. \u003cem\u003eFrontiers in Microbiology\u003c/em\u003e, \u003cem\u003e10\u003c/em\u003e, 1269.\u003c/li\u003e\n \u003cli\u003eTorres, M., Reina, J.C., Fuentes-Monteverde, J.C., Fern\u0026aacute;ndez, G., Rodr\u0026iacute;guez, J., Jim\u0026eacute;nez, C., \u0026amp; Llamas, I. (2018). AHL-lactonase expression in three marine pathogenic Vibrio spp. reduces virulence and mortality. PLoS ONE, 13(4), e0195176.\u003c/li\u003e\n \u003cli\u003eWest, S.A., Griffin, A.S., Gardner, A., \u0026amp; Diggle, S.P. (2006). Social evolution theory for microorganisms. Nature Reviews Microbiology, 4(8), 597\u0026ndash;607.\u003c/li\u003e\n \u003cli\u003eWhiteley, M., Diggle, S. P., \u0026amp; Greenberg, E. P. (2017). 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(2022). Exploring cocoa bean fermentation mechanisms by kinetic modelling. Royal Society Open Science, 9(2), 210274. https://doi.org/10.1098/rsos.210274\u003c/li\u003e\n \u003cli\u003eOsborne, J.P., \u0026amp; Edwards, C.G. (2018). A mathematical model of cocoa bean fermentation. Royal Society Open Science, 5(10), 180964. https://doi.org/10.1098/rsos.180964\u003c/li\u003e\n \u003cli\u003eSchwan, R.F., \u0026amp; Wheals, A.E. (2004). The microbiology of cocoa fermentation and its role in chocolate quality. Critical Reviews in Food Science and Nutrition, 44(4), 205\u0026ndash;221. https://doi.org/10.1080/10408690490464104\u003c/li\u003e\n \u003cli\u003eMorales, I., Moreira-Gonz\u0026aacute;lez, A.R., \u0026amp; Gal\u0026aacute;n-Vara, F. (2021). Dissecting fine-flavor cocoa bean fermentation through metabolomics analysis to break down the current metabolic paradigm. Scientific Reports, 11, 21904. https://doi.org/10.1038/s41598-021-01427-8\u003c/li\u003e\n \u003cli\u003eAdler, P., Bolten, C.J., Dohnt, K., Hansen, C.E., \u0026amp; Wittmann, C. (2014). The key to acetate: metabolic fluxes of acetic acid bacteria under cocoa pulp fermentation-simulating conditions. Applied and Environmental Microbiology, 80(15), 4702\u0026ndash;4716. https://doi.org/10.1128/AEM.01048-14\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"CSIR College of Science and Technology","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"quorum sensing, cocoa pulp juice fermentation, cocoa pulp wine, microbial succession, lactic acid bacteria, yeasts, acyl-homoserine lactones, autoinducer-2, farnesol, tyrosol, cross-kingdom signalling, precision fermentation, LC-MS/MS analysis","lastPublishedDoi":"10.21203/rs.3.rs-9583044/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9583044/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eCocoa pulp wine fermentation is governed by a dynamic succession of yeasts, lactic acid bacteria (LAB), and acetic acid bacteria (AAB) whose metabolic interactions determine ethanol yield, acidification trajectories, aroma precursor formation, and final beverage quality. Despite growing interest in precision fermentation, the role of quorum sensing (QS) \u0026mdash; the density-dependent chemical communication that coordinates collective microbial behaviour \u0026mdash; remains poorly characterised in cocoa pulp fermentation systems compared to other multispecies food fermentations. This systematic review synthesises current evidence on QS biomolecules relevant to bacteria and fungi, with the aim of establishing a mechanistic framework for understanding how interspecies signalling shapes fermentation performance and metabolite evolution in cocoa pulp wine.\u003c/p\u003e \u003cp\u003eFollowing a PRISMA-compliant literature search across Scopus, PubMed, and ScienceDirect (1985\u0026ndash;2025), peer-reviewed studies characterising QS molecules, signalling pathways, and their functional roles in food fermentation ecosystems were identified, screened, and narratively synthesised. Evidence was integrated across bacterial systems \u0026mdash; acyl-homoserine lactones (AHLs) in Gram-negative bacteria, LuxS-mediated autoinducer-2 (AI-2) signalling in LAB \u0026mdash; and fungal systems involving tyrosol and farnesol in yeasts, with comparative contextualisation from dairy, kimchi, and kombucha fermentations.\u003c/p\u003e \u003cp\u003eThe synthesis reveals that QS-mediated signalling regulates critical fermentation phenotypes including acidification kinetics, bacteriocin production, stress tolerance, biofilm formation, and cross-kingdom metabolic coordination in mixed-species consortia. Key evidence gaps persist regarding in situ QS signal quantification in cocoa pulp matrices, the functional consequences of polyphenol-mediated signal quenching, and the translation of QS dynamics into starter culture design. LC\u0026ndash;MS/MS is identified as the essential analytical platform for resolving QS molecule detection in complex food matrices. This review provides a foundational framework for integrating QS-guided microbial intelligence into precision cocoa fermentation strategies, opening a high-impact research frontier for the development of reproducible, high-value cocoa-based beverages.\u003c/p\u003e","manuscriptTitle":"Quorum Sensing Signalling Molecules in Cocoa Pulp Wine Fermentation: A Systematic Review","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-05-04 09:59:40","doi":"10.21203/rs.3.rs-9583044/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"997a5100-db4f-4edf-806d-1af740a02bf7","owner":[],"postedDate":"May 4th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":67358285,"name":"Applied \u0026 Industrial Microbiology"},{"id":67358286,"name":"General Microbiology"},{"id":67358287,"name":"Food Science \u0026 Technology"},{"id":67358288,"name":"Mycology"},{"id":67358289,"name":"Analytical Chemistry"}],"tags":[],"updatedAt":"2026-05-04T09:59:40+00:00","versionOfRecord":[],"versionCreatedAt":"2026-05-04 09:59:40","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-9583044","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-9583044","identity":"rs-9583044","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}