Next Article in Journal
Escherichia coli Nissle 1917 as a Probiotic Microbial Cell Factory: From Genetic Engineering to Fermentation
Previous Article in Journal
The Microbial Palette: From Bioprospecting to Genetic Engineering of Microbial Pigments
 
 
Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
Journals
Fermentation
Volume 12
Issue 6
10.3390/fermentation12060264
fermentation-logo
Submit to this Journal Review for this Journal Propose a Special Issue
► Article Menu

Article Menu

share Share announcement Help format_quote Cite question_answer Discuss in SciProfiles
first_page
settings
Order Article Reprints
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:
Background:
Review

Metabolomics-Driven Insights into Rice Wine Fermentation: From Descriptive Profiling to Intelligent Process Control

by
Baoyu Peng
1,2,
Bifeng Chen
3,4,
Zhaozhao Dai
1,2,5,
Jinwen Chen
2,
Lang Hu
1,2,
Lelei Wen
1,2 and
Changchun Li
1,2,*
1
Hubei Key Laboratory of Resource Utilization and Quality Control of Characteristic Crops, College of Life Science and Technology, Hubei Engineering University, Xiaogan 432000, China
2
College of Life Science and Technology, Hubei Engineering University, Xiaogan 432000, China
3
Research Center of Hubei Small Town Development, Hubei Engineering University, Xiaogan 432000, China
4
School of Architecture, Hubei Engineering University, Xiaogan 432000, China
5
College of Food Science & Technology, Huazhong Agricultural University, Wuhan 430070, China
*
Author to whom correspondence should be addressed.
Fermentation 2026, 12(6), 264; https://doi.org/10.3390/fermentation12060264
Submission received: 11 April 2026 / Revised: 18 May 2026 / Accepted: 25 May 2026 / Published: 29 May 2026
(This article belongs to the Special Issue Tradition to Innovation: Biotechnological and Microbial Approaches in Fermented Beverages)
Browse Figures
Versions Notes

Abstract

Rice wine fermentation involves complex biochemical dynamics that challenge traditional empirical control, highlighting the need for precise analytical characterization. This narrative review synthesizes the technological evolution of metabolomics from a descriptive tool to a driver of intelligent biomanufacturing. The progression from first-generation compositional profiling to third-generation strategies integrating high-resolution mass spectrometry, real-time sensing, multi-omics approaches, and artificial intelligence is delineated. This evolution has shifted research focus from static component cataloging to dynamic pathway elucidation, enabling deeper interpretation of flavor biosynthesis, functional metabolite formation, and accumulation of safety-related metabolites. Furthermore, this review critically analyzes how multi-omics integration reveals microbiome-metabolite interactions and provides mechanistic targets for quality regulation. Despite these advances, a gap remains between laboratory-scale analytical capabilities and industrial implementation. Key translational bottlenecks are identified, and a future roadmap toward AI-driven digital twin systems and real-time adaptive control is proposed. This framework positions metabolomics not merely as an analytical technique, but as a key foundation of next-generation smart fermentation strategies.

Graphical Abstract

1. Introduction

Rice wine is a traditional fermented food in East Asia, produced through sequential starch saccharification by filamentous fungi and alcoholic fermentation by yeast, as well as synergistic metabolism of complex microbial consortia [1]. This complicated process produces diverse metabolites including flavor-active esters and functional amino acids, together with potential safety-related components such as ethyl carbamate and biogenic amines [2]. The complex composition also leads to strong matrix effects, bringing great challenges to accurate qualitative and quantitative analysis [3]. As the rice wine industry shifts from traditional artisanal production to large-scale industrial and standardized manufacturing, empirical process control can no longer guarantee stable product quality, functional performance, and food safety [4]. This limitation mainly arises from the lack of precise analytical approaches to unravel metabolic dynamics and underlying regulatory mechanisms. Therefore, it is imperative to develop advanced strategies for comprehensive metabolite characterization.
Metabolomics, which aims to comprehensively profile small-molecule metabolites and elucidate metabolic pathways [5,6], has evolved transformatively to address relevant analytical demands. Early low-resolution approaches only achieved baseline profiling of dominant metabolites, with limitations in sensitivity, coverage, and resolution of trace or structurally similar compounds—restricting insights into complex metabolic interactions in rice wine. Commonly categorized into untargeted, targeted, and semi-targeted approaches, metabolomics serves distinct purposes: untargeted metabolomics enables unbiased global profiling to explore undiscovered flavor compounds and overall metabolic characteristics; targeted metabolomics provides high sensitivity and precise quantification for key amino acids and safety markers (e.g., ethyl carbamate, biogenic amines); semi-targeted metabolomics integrates both advantages for simultaneous large-scale identification and quantification of flavor-related metabolites in complex rice wine matrices [7]. Advances in high-resolution mass spectrometry and chromatographic separation improved metabolite coverage and quantification accuracy [8], while recent integration of intelligence (AI)-assisted data analysis and multi-omics enhanced pattern recognition and mechanistic interpretation [9]. Despite these progresses, gaps remain in translating technological advances into industrial applications, highlighting the need for a critical synthesis of metabolomics evolution in rice wine research.
This review critically summarizes the application and technological evolution of metabolomics in rice wine fermentation, moving beyond mere compositional description to highlight gaps between analytical capability and industrial demand. Specifically, it evaluates the advantages and limitations of successive metabolomics generations in the rice wine matrix, explores technology-driven insights into flavor formation, functional transformation, and safety risk regulation, analyzes metabolomics-based microbe-metabolite interaction research, and identifies bottlenecks and future directions for industrial translation. By framing rice wine as a representative, analytically challenging model for complex fermented foods, this review provides methodological guidance for metabolomics-driven research in food chemistry, with broader implications for traditional fermented food industrialization.
Literature search and selection strategy. This manuscript was prepared as a narrative review rather than a systematic or scoping review. Relevant literature was retrieved from Web of Science, PubMed, Scopus, and Google Scholar using combinations of keywords including “rice wine”, “Huangjiu”, “Jiuqu”, “microbial community”, “microbial succession”, “fermentation”, “metabolomics”, “multi-omics”, “metabolite profiling”, “flavor formation”, “flavor compounds”, “artificial intelligence”, “machine learning”, “industrialization”. The search mainly covered studies published between 2000 and 2026, with emphasis on publications from the past decade; earlier landmark studies were also included when necessary to explain fundamental mechanisms or historical development. Original research articles, review articles, and authoritative monographs closely related to rice wine fermentation, microbial metabolism, metabolomics technologies, flavor formation mechanisms, and quality regulation were included. Conference abstracts, patents, duplicated publications, and studies not directly related to rice wine fermentation, microbial metabolism, flavor chemistry, omics analysis, or data-driven flavor prediction were excluded.

2. Metabolomics Development in Rice Wine Fermentation

2.1. First-Generation Metabolomics (2000–2010): Baseline Profiling of Dominant Metabolites

To understand the current state-of-the-art, it is imperative to trace the conceptual evolution of metabolomics in rice wine fermentation, moving from simple descriptive profiling toward the contemporary goal of intelligent process control (Figure 1). The first generation of metabolomics research on rice wine fermentation (2000–2010) was in its exploratory stage, focusing on baseline profiling of dominant metabolites. Its technical foundation centered on core analytical platforms: low-resolution gas chromatography-mass spectrometry (GC-MS) [10], low-resolution liquid chromatography-mass spectrometry (LC-MS) [11], and nuclear magnetic resonance (NMR) spectroscopy [12,13,14]. For GC-MS, chemical derivatization was often necessary, enabling rapid screening of volatile metabolites such as ethanol and major esters [15]. Low-resolution LC-MS avoided derivatization for direct analysis of nonvolatile and polar metabolites, while NMR offered nondestructive detection and minimal sample preparation for rapid global metabolic fingerprinting in rice wine’s complex matrix [16,17].

2.1.1. Technical Basis and Analytical Limitations

Despite these unique advantages for targeted and holistic metabolite detection, first-generation metabolomics techniques exhibited prominent analytical limitations in rice wine research [18]. First, the analytical sensitivity was insufficient to detect trace metabolites at and below the microgram-per-liter level, leading to narrow metabolic coverage of the fermentation system [19]. Second, structural isomers and low-abundance endogenous metabolites were difficult to achieve effective resolution, and the entire data processing workflow relied heavily on manual peak identification and expert subjective interpretation, resulting in low analysis efficiency [20]. Furthermore, the single analytical platform applied in most studies restricted the simultaneous and comprehensive analysis of both volatile and nonvolatile metabolites, which fundamentally constrained the holistic interpretation of the complex metabolic networks underlying rice wine fermentation [13,21,22].

2.1.2. Application in Rice Wine: Profiling Major Metabolic Trends

Notwithstanding the aforementioned analytical limitations, first-generation metabolomics still laid a critical foundational groundwork for rice wine fermentation research, with its primary application focused on profiling the temporal variation characteristics of dominant metabolites during the entire fermentation process. Combined analyses of GC-MS and LC-MS characterized the dynamic changes in core metabolites (e.g., ethanol, lactic acid, acetic acid) across the saccharification, alcoholic fermentation, and post-fermentation stages, and further elucidated the complementary metabolic roles of filamentous fungi and yeast in the rice wine microecosystem [23,24]. In flavor-related research, this analytical system identified major flavor-active substances including ethyl acetate, isoamyl alcohol, and isobutanol, and established the correlation between their concentration ranges and the basic sensory attributes of rice wine [25].
NMR-based metabolic fingerprinting further complemented the above research by enabling macroscopic monitoring of rice wine fermentation dynamics. It also supported the preliminary reconstruction of core metabolic pathways from starch degradation to sugar utilization and final alcohol formation [14]. Although these studies lacked in-depth mechanistic exploration and failed to access trace metabolites, the baseline metabolic datasets obtained laid essential reference frameworks for the subsequent development of high-resolution and integrative metabolomics techniques in rice wine research [26,27].

2.2. Second-Generation Metabolomics (2010–2020): Comprehensive Flavor and Safety Analysis

Driven by the industrial demand for in-depth characterization of rice wine metabolites and the academic need to explore fermentation mechanisms, second-generation metabolomics (2010–2020) emerged as a transformative approach in this research field. It addressed the key limitations of first-generation techniques and shifted the research focus from simple baseline profiling of dominant metabolites to the comprehensive and quantitative analysis of flavor compounds and safety risk metabolites, marking a critical step toward the systematic study of rice wine fermentation chemistry.

2.2.1. Advances in High-Resolution Mass Spectrometry

The technological core of this transformation was the widespread adoption of high-resolution mass spectrometry (HRMS) platforms, including high-resolution LC-MS/MS and GC-MS/MS [28,29,30,31]. These platforms featured significantly enhanced mass accuracy, analytical sensitivity, and dynamic range, which facilitated the reliable qualitative and quantitative analysis of trace metabolites in rice wine at the nanogram-per-liter level. Among them, high-resolution LC-MS/MS combined with electrospray ionization (ESI) and multiple reaction monitoring (MRM) modes, realized the sensitive detection of diverse nonvolatile and semi-volatile metabolites without complex chemical derivatization [32], greatly improving the analysis efficiency for polar and macromolecular metabolites.
These advances in HRMS were particularly critical for rice wine safety-related research. Coupling high-resolution MS with solid-phase extraction (SPE) pretreatment significantly lowered the detection limit for ethyl carbamate—a typical safety risk metabolite in fermented wines—thus enabling the precise characterization of its rapid accumulation law in the late fermentation stage and its dependence on raw material properties and processing parameters [31,33]. In the field of volatile flavor analysis, GC-MS/MS combined with headspace solid-phase microextraction (HS-SPME) achieved the accurate quantification of trace aroma-active compounds such as limonene and linalool, effectively solving the problems of poor specificity and low reproducibility in the analysis of trace volatiles in first-generation techniques, and laying a foundation for the in-depth study of rice wine flavor complexity [20].

2.2.2. Multi-Technique Integration and Expanded Metabolite Coverage

Beyond the advancement of individual HRMS platforms, a defining hallmark of second-generation metabolomics was the strategic integration of multiple analytical techniques and optimization of sample preparation workflows, which were specifically designed to mitigate matrix interferences in rice wine and further expand metabolite coverage [34]. The combined application of GC-MS, LC-MS, and NMR spectroscopy, together with optimized pretreatment methods such as QuEChERS (Quick, Easy, Cheap, Effective, Rugged, Safe) and selective solid-phase extraction, not only compensated for the inherent limitations of a single platform in metabolite detection but also significantly improved the analytical robustness of the entire system [35,36]. For example, QuEChERS, a classic sample pretreatment purification technique, enabled the simultaneous and high-throughput quantification of esters, biogenic amines, and ethyl carbamate with low organic solvent consumption [29], meeting the demand for the simultaneous analysis of multiple types of metabolites in rice wine.
The cross-integration of mass spectrometry and NMR spectroscopy further improved the confidence in the identification of unknown metabolites in rice wine [22]. The accurate mass and detailed fragmentation information obtained from MS analysis were complemented by the structural insights of functional groups from NMR spectroscopy, realizing the mutual validation of metabolite identification results. This multi-technique coupling strategy enabled the efficient structural elucidation of complex endogenous metabolites in rice wine, supporting the systematic and comprehensive characterization of the overall chemical composition of rice wine for the first time [37].

2.2.3. Enabling Metabolic Pathway Elucidation

The technical advances in high-resolution detection and multi-platform integration ultimately enabled a paradigm shift in rice wine metabolomics research—moving beyond mere component screening to the systematic elucidation of metabolic pathways underlying flavor formation and safety risk metabolite production. Time-resolved quantitative metabolomic data, combined with metabolic pathway databases and bioinformatic analysis, facilitated the complete reconstruction of core flavor formation pathways in rice wine, with fatty acid metabolism and amino acid conversion as the two central axes [38,39]. Specifically, lipase-mediated lipid hydrolysis, β-oxidation to acetyl-CoA, and subsequent esterification with ethanol were identified as the key biosynthetic routes for ester compounds; meanwhile, the catabolism of branched-chain amino acids was confirmed to be the main metabolic pathway for the production of higher alcohols such as isoamyl alcohol and isobutanol [40,41,42].
In parallel with flavor mechanism research, second-generation metabolomics also clarified the biochemical basis for the formation of major safety risk metabolites in rice wine [43]. For ethyl carbamate, its formation was found to be centered on urea derived from arginine catabolism and its acid-catalyzed condensation reaction with ethanol; for biogenic amines, their accumulation was attributed to the decarboxylation of free amino acids by specific microbial taxa including lactic acid bacteria and enterobacteria [44,45]. These in-depth mechanistic insights not only enriched the theoretical system of rice wine fermentation biochemistry but also provided a solid theoretical foundation for the development of targeted process control strategies and safety risk mitigation measures in industrial rice wine production.

2.3. Third-Generation Metabolomics (2020-Present): AI-Driven and Multi-Omics-Enabled Precision Analysis

Building on the comprehensive metabolic pathway elucidation achieved by second-generation metabolomics, third-generation metabolomics (2020-present) has ushered in a new era of precision analysis for rice wine fermentation research. Its core technological advances lie in the triad of artificial intelligence (AI)-assisted high-dimensional data mining, multi-omics integrative analysis, and emerging in situ real-time detection strategies [36,46]. These innovations have broken through the limitations of single-technology and offline analysis, enabling the research paradigm to shift from static pathway characterization to dynamic, predictive, and mechanistically deep precision analysis of the rice wine fermentation system.

2.3.1. AI-Assisted Data Analysis and Predictive Modeling

The explosive growth of high-dimensional metabolomic data generated by high-resolution mass spectrometry platforms has created an urgent demand for efficient data processing and interpretation, which has driven the increasing and strategic integration of AI into the entire metabolomics workflow since 2020 [1]. Classic machine learning (ML) algorithms, including random forest and support vector machines, have been widely applied to facilitate the efficient screening and identification of key metabolites that are closely associated with fermentation stage transformation, product quality attributes, and potential safety risks [47]. More advanced deep learning (DL) models have further enhanced the accuracy of metabolic pattern recognition and the reliability of temporal dynamic modeling for the complex fermentation system.
Notably, the adoption of interpretable AI approaches has addressed the “black box” problem of traditional machine learning models, which enhanced the interpretability and transparency of modeling results by visualizing the feature importance of key metabolites and their regulatory associations with metabolic pathways. This advancement has facilitated the translation of pure data-driven outputs into biologically meaningful scientific hypotheses [47,48]. Collectively, these AI-based improvements have fundamentally transformed metabolomics research in rice wine fermentation—shifting it from correlation-based phenotypic analysis to predictive and mechanism-oriented modeling [47].

2.3.2. Multi-Omics Integration Linking Microbes to Metabolites

A defining characteristic that further distinguishes third-generation metabolomics is the seamless integration of metabolomics with metagenomics and transcriptomics, which enables the establishment of causal and regulatory links between microbial community structure, functional gene expression, and metabolite biosynthesis [49,50,51]. This integrative framework assigns clear functional roles to each omics layer: metagenomics deciphers the functional gene pool of the fermentation microbiome, metabolomics provides direct phenotypic readouts of metabolic activity, and transcriptomics captures the dynamic regulatory responses of microbial communities to fermentation environmental changes. Together, these layers support the systematic construction of complete microbe-gene-metabolite (MGM) regulatory networks [7].
Such multi-omics integrative approaches have enabled the accurate identification of core functional microbial taxa and key molecular targets that govern flavor metabolite formation and safety-related metabolite accumulation in rice wine fermentation [36]. The mechanistic insights obtained from these networks not only deepen the understanding of microbial-metabolic interactions but also provide precise molecular targets for microbial community modulation and targeted precision fermentation regulation [52,53,54].

2.3.3. Emerging In Situ and Real-Time Analytical Strategies

Frontier research is exploring in situ detection and real-time metabolite analysis to support dynamic fermentation monitoring [55]. Microfluidic platforms and in situ mass spectrometry techniques, including probe electrospray ionization, enable rapid detection of volatile and nonvolatile metabolites with minimal sample preparation [47,56,57]. Although challenges remain in sensitivity, stability, and industrial scalability, these approaches offer strong potential for online monitoring, early risk warning, and real-time process adjustment [58]. The transition toward third-generation metabolomics is characterized by a dramatic leap in analytical precision. A comparative evaluation of mainstream and emerging platforms, regarding their sensitivity and throughput, is detailed in Table 1. Furthermore, the strategic selection of these tools—particularly regarding their spatial and temporal resolution—is visually mapped in the analytical landscape shown in Figure 2.
Overall, these advances have progressively expanded the scope of metabolomics in rice wine fermentation, moving beyond simple metabolite detection toward deeper biological interpretation [58]. As illustrated in Figure 1, this evolution reflects a clear conceptual shift from empirical observation and descriptive profiling to mechanism-oriented analysis and, ultimately, predictive and intelligent process control. While emerging techniques like spatial metabolomics (MSI) and real-time sensing offer unprecedented insights into spatiotemporal dynamics, classical platforms remain vital for standardized quantification [7]. This technological transition lays the foundation for the precision monitoring systems discussed in the following sections.

3. Metabolite Profiling and Safety Monitoring in Rice Wine Production

3.1. Elucidation of Flavor Metabolite Biosynthesis

The characteristic flavor of rice wine arises from the coordinated production of hundreds of volatile and nonvolatile metabolites [63,64]. Esters, higher alcohols, aldehydes, ketones, and terpenes constitute the principal chemical basis of aroma and taste. With the advancement of metabolomics, research has evolved from descriptive cataloging of flavor components toward systematic elucidation of biosynthetic mechanisms, with successive technological generations progressively uncovering the complexity of flavor formation [64].
The flavor landscape of rice wine is highly heterogeneous, primarily dictated by the specific Qu (starter culture) and raw materials used. Before dissecting specific biosynthetic pathways, it is essential to categorize the distinct metabolic fingerprints associated with major rice wine varieties. The inherent complexity of rice wine flavor is highly dependent on the starter culture and regional processing methods [7]. Table 2 provides a comprehensive overview of the dominant microbiota and key metabolic signatures (biomarkers) that distinguish representative varieties such as Shaoxing Huangjiu and Red Qu wine; regional variations exhibit unique biomarker profiles ranging from specific amino acids to functional polyketides, which directly correlate with their dominant microbial consortia.
Early metabolomic analyses enabled preliminary identification and quantification of high-abundance flavor compounds and revealed their temporal dynamics during fermentation [7]. Typical examples include ethyl acetate and isoamyl alcohol, whose concentration changes were correlated with basic sensory attributes [65]. However, limited sensitivity prevented detection of trace aroma compounds, and pathway interpretation remained largely phenomenological, lacking insight into key regulatory steps.
Technological advancements, particularly the adoption of high-resolution mass spectrometry, overcame these limitations by enabling reliable detection of trace-level metabolites [66]. The combination of high-resolution GC-MS/MS with headspace solid-phase microextraction allowed quantification of terpenes such as limonene and linalool at the nanogram-per-gram level [65], revealing that terpenes originate from both rice raw materials and Aspergillus species used in koji production, contributing significantly to floral and fruity aromas. Aldehydes and ketones were shown to derive primarily from early-stage sugar metabolism and amino acid transformations, with excessive accumulation leading to pungent off-odors; their formation and conversion were strongly influenced by fermentation temperature and pH [1]. Integration of high-resolution LC-MS/MS with pathway analysis clarified the central roles of fatty acid metabolism and amino acid catabolism in ester and higher alcohol formation, identifying alcohol acyltransferases and branched-chain amino acid decarboxylases as key regulatory enzymes [41]. Combined GC- and LC-based platforms enabled comprehensive coverage of both volatile and nonvolatile flavor metabolites, supporting systematic evaluation of overall flavor quality.
Recent progress has further integrated artificial intelligence with multi-omics approaches, advancing flavor research toward mechanistic interpretation and predictive control [1,67]. Machine learning algorithms, including random forest and gradient-boosting models, have been applied to screen key flavor-related metabolites from high-dimensional flavoromics datasets and to model relationships between chemical profiles and sensory quality [48]. However, the reported model performance is highly dependent on dataset size, sample composition, number of classes or sensory descriptors, validation strategy, and whether independent external validation was performed. Therefore, these studies should be interpreted as demonstrating the potential of machine learning for flavor prediction and metabolite screening rather than establishing a broadly generalizable accuracy benchmark. Explainable AI approaches further enable visualization of flavor-related metabolic pathways and reveal associations between microbial gene expression, enzymatic activities, and the accumulation of corresponding flavor compounds [68,69]. Multi-omics integration establishes regulatory networks linking microbial community structure, gene expression, and metabolite production, providing new avenues for directed flavor modulation [66,70]. Beyond simple identification, integrating metabolomics data into biological networks allows for the mapping of metabolic flux, as illustrated in the integrative multi-omics framework in Figure 3, which elucidates the core pathways from glycolysis to the divergent synthesis of esters and peptides across diverse fermentation systems.
Table 2. Comparative overview of dominant microbiota, key metabolic signatures, and distinct sensory attributes across representative regional rice wines.
Table 2. Comparative overview of dominant microbiota, key metabolic signatures, and distinct sensory attributes across representative regional rice wines.
Rice Wine CategoryKey Raw MaterialsDominant Microflora (Starter/Qu)Major Metabolite ClassesUnique Biomarkers (Discriminants)Sensory DescriptorsReference
Shaoxing Huangjiu (Yellow Rice Wine)Glutinous rice, Wheat Qu (Saccharification starter), WaterAspergillus oryzae, Saccharomyces cerevisiae, Lactobacillus spp.Amino acids, Organic acids, Short-chain peptides, Estersγ-Aminobutyric acid (GABA), Pyroglutamic acid, Aspartic acid, Diethyl succinate, BenzaldehydeUmami, Full-bodied, Mellow, Rich bouquet[71]
Red Qu Rice Wine (Hong Qu)Glutinous rice, Red Yeast Rice (Monascus starter)Monascus purpureus, Monascus ruber, Saccharomyces cerevisiaePolyketides, Pigments, Statins, Volatile estersMonacolin K, Monascin, Ankaflavin, Ethyl hexanoate, β-PhellandreneFunctional, Fruity, Slightly bitter, Distinctive red color[72]
Sweet Rice Wine (Jiuniang/Tianjiu)Polished glutinous rice, Xiaoqu (Rice Qu)Rhizopus spp. (e.g., R. oryzae), Mucor spp., Rhizopus chinensisReducing sugars, Alcohols, Organic acidsGlucose, Maltose, Ethyl lactate, 2-Phenylethanol, Isoamyl alcoholSweet, Floral, Honey-like, Low alcohol, Light body[73]
Black Glutinous Rice WineBlack glutinous rice (Whole grain), Wheat Qu or XiaoquSaccharomyces cerevisiae, Rhizopus spp., Aspergillus spp.Anthocyanins, Phenolic acids, Flavanols, Citric acidCyanidin-3-glucoside, Peonidin-3-glucoside, Protocatechuic acid, Vanillic acidAstringent, Antioxidant-rich, Complex berry notes[74]

3.2. Functional Metabolites

Functional metabolites in rice wine, including polyphenols, amino acids, and γ-aminobutyric acid (GABA), contribute antioxidant, anti-inflammatory, and neuroactive properties and form the basis of its health-promoting value [1,7]. Advances in metabolomics have driven a transition from simple component screening toward integrated elucidation of biosynthetic mechanisms, physiological activities, and regulatory pathways, thereby supporting the development of functional rice wine products [66].
Early analyses enabled qualitative identification of abundant amino acids and polysaccharides, revealing approximately 18 amino acids, with essential amino acids accounting for 30–35% of the total [41]. Polyphenols were detected but not accurately quantified or structurally resolved. Biological activity assessments were largely limited to in vitro antioxidant assays, resulting in weak links between chemical composition and physiological function and providing limited guidance for product development [75].
The introduction of high-resolution LC-MS/MS, combined with solid-phase extraction, enabled precise identification and quantification of trace polyphenols [76]. Catechin and epicatechin were quantified at concentrations ranging from 0.1 to 5 μg g−1, and novel derivatives such as 5-hydroxyferulic acid glucoside were identified [77]. These studies established a positive correlation between polyphenol content and antioxidant capacity and identified catechin and epicatechin as major contributors. GABA was accurately quantified at 10–25 μg g−1, and its biosynthesis was traced to glutamate decarboxylation catalyzed by glutamate decarboxylase, with documented roles in neurotransmitter modulation and blood pressure regulation [78]. Pathway analyses clarified that polyphenols originate from phenylalanine in rice and are transformed by enzymatic activities of Aspergillus during koji production, with phenylalanine ammonia lyase initiating conversion to cinnamic acid followed by hydroxylation and methylation reactions [79]. GABA synthesis was shown to be maximized at approximately 30 °C and pH 4.5–5.0 under anaerobic conditions, enabling two- to threefold increases through process optimization [78].
Current multi-omics integration links biosynthetic mechanisms, regulatory pathways, and physiological activity into a unified framework. Combined metabolomic and transcriptomic analyses identify phenylalanine ammonia lyase and cinnamate hydroxylase genes as central regulators of polyphenol synthesis [64,80,81]. Nutritional and bioavailability studies further demonstrate that rice wine polyphenols can be absorbed and exert biological effects by scavenging free radicals and suppressing inflammatory mediators [82,83]. Artificial intelligence accelerates screening of candidate functional metabolites and predicts biosynthetic efficiency and bioactivity, facilitating precision development of functional rice wine products [84].

3.3. Safety Risk Metabolites

Potential safety risks in rice wine fermentation include ethyl carbamate, biogenic amines, and mycotoxins, which represent major constraints on industrial standardization and safe scale-up [85]. Progressive development of metabolomics has established a technical framework spanning trace detection, mechanistic elucidation, and process-level control, enabling precise prevention and mitigation of safety risks [86].
Early detection methods lacked sufficient sensitivity and were limited to monitoring relatively abundant compounds, such as higher alcohols [10,23,87]. Trace-level hazards, including ethyl carbamate, biogenic amines, and mycotoxins, were often undetectable, and safety control relied largely on empirical adjustments and raw material selection, resulting in limited effectiveness [51,88].
The emergence of high-resolution mass spectrometry broke these barriers. High-resolution LC-MS/MS combined with solid-phase extraction reduced the detection limit for ethyl carbamate to approximately 0.5 ng L−1 and revealed its accumulation during late fermentation and post-fermentation stages [31,45,89]. These data clarified the formation pathway involving the reaction of urea with ethanol and provided a scientific basis for targeted process control. High-resolution LC-MS/MS combined with QuEChERS extraction enabled simultaneous quantification of eight major biogenic amines with detection limits near 1 ng g−1, demonstrating that their formation is driven by amino acid decarboxylation catalyzed by lactic acid bacteria and enteric bacteria [90]. Histamine and tyramine were identified as the most toxic, with elevated levels associated with overgrowth of harmful microorganisms [91]. Trace detection of aflatoxin B1 and ochratoxin A further enabled effective screening of raw material contamination [88,92].
Contemporary approaches integrate artificial intelligence and multi-omics to establish end-to-end safety control systems encompassing detection, mechanism analysis, intervention, and early warning [52]. Support vector machine models predicting ethyl carbamate formation based on raw material arginine content and fermentation temperature achieve prediction errors below 5%, enabling proactive risk management [43,86]. Multi-omics analyses reveal correlations between histidine decarboxylase genes in enteric bacteria, tyrosine decarboxylase genes in lactic acid bacteria, and biogenic amine accumulation, supporting source-level risk control through microbial regulation [93]. In situ mass spectrometry further enables real-time monitoring of risk dynamics, facilitating timely interventions.

3.4. Adaptive Safety Monitoring Systems

The complexity of rice wine fermentation and the unique characteristics of its matrix necessitate adaptable analytical strategies. Metabolomics platforms differ in throughput, sensitivity, cost, and suitability across production stages, requiring stage-specific optimization to achieve comprehensive safety monitoring [94].
Before fermentation, raw material screening focuses on mycotoxins, heavy metals, and pesticide residues [43]. High-resolution LC-MS/MS combined with solid-phase extraction is preferred for trace-level detection, while NMR spectroscopy provides rapid preliminary screening for large sample sets, followed by confirmatory high-resolution MS analysis to balance efficiency and accuracy.
During fermentation, dynamic monitoring of risk accumulation and metabolic anomalies is essential for early warning. Combined application of high-resolution LC-MS/MS and GC-MS/MS enables simultaneous tracking of flavor metabolites and hazard factors, generating accurate temporal profiles [43,95]. In situ mass spectrometry allows minute-level real-time monitoring of core metabolites such as ethanol and ethyl carbamate, although its limited throughput necessitates complementary use with conventional high-resolution methods.
After fermentation, emphasis shifts to finished product safety verification and quality assessment. Integrated high-resolution LC-MS/MS and GC-MS/MS platforms are optimal for comprehensive quantification of diverse risk factors. QuEChERS and solid-phase extraction effectively reduce matrix interference and maintain relative standard deviations below 5% [51,96]. For large-scale production, AI-assisted rapid detection models further enhance throughput and reduce analysis time. Metabolomics has been applied across multiple dimensions of rice wine fermentation, ranging from flavor formation and functional compound biosynthesis to safety risk assessment and process monitoring [52], with representative applications and their practical implications summarized in Table 3, which highlights contributions to ensuring product consistency and authenticity.
Overall, comparative evaluation indicates that high-resolution mass spectrometry should serve as the core analytical technology due to its superior sensitivity, accuracy, and multi-analyte capability, despite higher cost and operational complexity [43]. NMR is well suited for rapid screening and routine monitoring, while in situ techniques are valuable for dynamic process control [67]. In practice, a balanced multi-technology strategy that accounts for analytical objectives, cost constraints, and efficiency requirements is essential for establishing a comprehensive, full-process safety monitoring system.

4. Application of Metabolomics in Microbiome-Metabolite Interaction Control

4.1. Evolution of Analytical Frameworks Linking Microbial Community Structure and Metabolites

Rice wine fermentation is driven by cooperative metabolism within a dynamic microbial consortium [55]. Successional shifts in community composition directly determine the identity and abundance of metabolites, making elucidation of microbiome-metabolite interactions central to understanding fermentation mechanisms [37]. Progress in metabolomics has promoted a transition from macroscopic correlation analysis toward molecular-level mechanistic interpretation, establishing an integrated analytical framework encompassing community characterization, metabolite association, and pathway elucidation [9]. Across successive technological generations, analytical resolution and explanatory power have improved in a clear stepwise manner.
Early studies combining classical isolation and culture-based microbiology with basic metabolite profiling established coarse taxon-metabolite associations [98,99]. Saccharomyces species were linked to ethanol and ethyl acetate production, while Aspergillus oryzae was associated with starch saccharification and organic acid accumulation [1]. However, these approaches were restricted to cultivable microorganisms and a limited set of high-abundance metabolites, leaving interaction analysis largely descriptive and lacking mechanistic depth.
The integration of high-resolution metabolomics with high-throughput sequencing overcame these constraints. Sequencing technologies enabled comprehensive detection of both cultivable and uncultivable taxa and revealed characteristic succession patterns during fermentation [100]. Aspergillus oryzae typically dominates the saccharification stage, accounting for more than 60% of the community; Saccharomyces becomes predominant during alcoholic fermentation; and lactic acid bacteria increase in relative abundance during late fermentation, engaging in cooperative metabolism with yeast [3,66]. High-resolution metabolomic data corroborate these functional roles, demonstrating that Saccharomyces drives ethanol and ester biosynthesis, lactic acid bacteria contribute to organic acid pools and partial biogenic amine formation, and Aspergillus regulates starch hydrolysis and amino acid precursor supply [47].
Multi-omics integration has further extended interaction analysis to the molecular level [70]. Joint analysis of metabolomic, metagenomic, and transcriptomic datasets enables construction of microbiome-gene-metabolite regulatory networks that precisely localize key taxa, genes, and pathways [101,102]. For example, expression of the alcohol dehydrogenase gene ADH1 in Saccharomyces increases more than tenfold during the main fermentation stage and shows a strong positive correlation with ethanol accumulation, providing direct molecular evidence for ethanol biosynthesis and a clear target for precise fermentation control [103]. Understanding rice wine fermentation as a complex biological system requires integration of metabolomics with microbial and genetic information [54]. Elucidating the ‘microbiome-metabolite’ nexus requires advanced computational frameworks to correlate microbial succession with chemical changes [54]. The multi-omics-driven regulatory framework illustrated in Figure 4 outlines the systemic approach needed to identify core functional taxa and their metabolic contributions, presenting a system-level framework that links microbial community dynamics, gene expression, metabolic outputs, and fermentation phenotypes through multi-omics integration. This integrative perspective enables the identification of key regulatory nodes that cannot be resolved through metabolomics alone [104].

4.2. Metabolic Contributions of Core Functional Taxa and Strategies for Regulation

The core functional microbiota in rice wine fermentation comprise filamentous fungi, yeasts, and lactic acid bacteria, each performing distinct but complementary metabolic roles that collectively determine product quality [37]. Metabolomics enables quantitative attribution of metabolite pools to specific taxa and supports a shift from empirical management toward precision regulation.
Aspergillus oryzae functions as the initial key taxon by driving saccharification and precursor generation. Through secretion of hydrolases, it converts starch, proteins, and lipids into glucose, free amino acids, and other low-molecular-weight compounds that sustain downstream microbial metabolism [105]. By the end of saccharification, glucose concentrations may increase from 2.1% (w/w) to 18.5% (w/w), while total free amino acids may reach 2.3 g L−1. Under optimized conditions of approximately 30 °C, 85% relative humidity, and pH 5.0, amylase activity reaches its maximum and saccharification efficiency improves by over 20% [3,106]. Concurrently, polyphenol and γ-aminobutyric acid contents increase by approximately 15% and 25%, respectively.
Saccharomyces species are the principal producers of ethanol and core ester-type flavor compounds [107]. Glycolytic flux supports ethanol production, while downstream fatty acid and amino acid metabolism generates esters and higher alcohols [108]. Ethanol typically constitutes 12–15% of the total metabolite pool, and ethyl acetate accounts for more than 60% of the total ester fraction [66]. Regulatory strategies focus on strain selection and fine-tuning of process parameters [109]. Screening for high ester-producing strains can increase ethyl acetate yields by approximately 30% while reducing isoamyl alcohol formation by around 25% [101]. Microaerobic conditions at approximately 28 °C have been identified as favorable for simultaneous optimization of flavor development and alcohol yield.
Lactic acid bacteria primarily contribute to organic acid formation and flavor balance. Lactate generally represents more than 70% of total organic acids, and concentrations of 1.0–1.5 g L−1 enhance flavor complexity while inhibiting undesirable microorganisms [110,111]. Although some lactic acid bacteria can produce biogenic amines, selected strains—such as Lactobacillus plantarum—can generate lactate at approximately 1.3 g L−1 without detectable biogenic amine formation [112]. Early inoculation with such strains can reduce biogenic amine accumulation by more than 40%, improving both sensory quality and safety [113,114].
Effective quality improvement relies on coordinated regulation of these core taxa rather than single-organism interventions. Metabolomics-based dynamic monitoring of metabolite trajectories enables optimization of fermentation parameters to balance the activities of Aspergillus, Saccharomyces, and lactic acid bacteria, promoting beneficial metabolite formation while suppressing harmful accumulation. This integrated strategy allows simultaneous enhancement of flavor, functionality, and safety.

4.3. Suppression of Harmful Taxa and Purification of the Fermentation Ecosystem

Harmful taxa, including enteric bacteria and mycotoxigenic fungi, pose health risks and exert competitive pressure on beneficial microorganisms through production of biogenic amines and mycotoxins [115]. Iterative advances in metabolomics have enabled development of a comprehensive workflow for identification, monitoring, and suppression of harmful taxa, supporting targeted purification of the fermentation ecosystem.
High-resolution metabolomics combined with high-throughput sequencing enables accurate detection and quantification of harmful taxa and their metabolic outputs. Sequencing data reveal the abundance of taxa such as Enterobacteriaceae and Aspergillus flavus, while high-resolution mass spectrometry identifies corresponding metabolite signatures. Correlation analyses demonstrate that when Enterobacteriaceae exceed approximately 5% of the microbial community, biogenic amine concentrations often surpass safety thresholds [91,116]. The presence of Aspergillus flavus and Penicillium species is closely associated with production of potent carcinogenic mycotoxins, allowing rapid assessment of contamination severity through integrated analysis [117].
Suppression strategies include source control, modulation of fermentation conditions, and biological inhibition, with metabolomics providing objective evaluation of intervention efficacy [114]. Raw material selection and disinfection can reduce aflatoxin B1 levels from approximately 0.8 ng g−1 to below 0.1 ng g−1 and decrease initial fungal contamination by around 80% [88]. Adjusting fermentation pH to 4.5–5.0 inhibits Enterobacteriaceae growth and reduces biogenic amine accumulation by more than 35%, while anaerobic conditions further suppress proliferation of mycotoxigenic fungi [116].
Biological inhibition offers high specificity and safety. Bacillus subtilis secretes antimicrobial peptides that can reduce Enterobacteriaceae abundance by over 60% and render aflatoxin B1 undetectable [114]. Plant-derived polyphenols and cinnamon extracts inhibit key enzymatic activities of harmful taxa, reducing risk factor formation while enhancing antioxidant capacity [43]. Metabolomics enables the targeted quantification of residual safety-risk metabolites, defined here as undesirable compounds or precursors that persist or accumulate during fermentation, aging, or after control interventions. In rice wine fermentation, these may include ethyl carbamate and its precursors, biogenic amines, excessive higher alcohols, and other safety-related by-products. By monitoring both risk metabolites and desirable flavor compounds, metabolomics can identify critical accumulation stages and guide the optimization of inhibitor dosage, addition timing, or microbial regulation strategies, thereby improving suppression efficiency without compromising sensory quality.
Integration of in situ monitoring and artificial intelligence further enables dynamic surveillance and responsive control. Real-time mass spectrometry tracks concentrations of hazard metabolites and supports immediate adjustment of process parameters or targeted addition of inhibitors when anomalies are detected [7]. Machine learning models trained on historical fermentation data predict growth trends of harmful taxa and enable preemptive interventions. Together, these approaches establish a full-process prevention and control system that continuously purifies the fermentation ecosystem and safeguards product quality and safety.

5. Metabolomics Technology Application Bottlenecks and Future Prospects

5.1. Key Bottlenecks Limiting Translation of Metabolomics into Practice

Despite substantial progress, several fundamental bottlenecks still limit the effective translation of metabolomics from laboratory-scale research to routine industrial application in rice wine fermentation. Technically, the intrinsic complexity of the fermentation matrix continues to challenge analytical robustness. Matrix effects cannot yet be fully eliminated, and commonly used pretreatment strategies, such as solid-phase extraction and QuEChERS, show limited selectivity toward novel trace metabolites and structural isomers [63,118]. In situ and real-time detection technologies remain at an early stage of development and face inherent trade-offs among sensitivity, throughput, robustness, and cost, restricting their deployment in industrial environments.
At the data and methodology level, multi-omics datasets are high-dimensional, heterogeneous, and strongly context-dependent. Current analytical frameworks largely rely on statistical correlations, while causal relationships between microbial functions, gene expression, and metabolite formation remain insufficiently resolved [7]. Moreover, models trained on site-specific datasets often exhibit limited transferability across raw materials, production scales, and geographical regions, posing challenges for reproducibility and generalization [36]. The lack of standardized data processing pipelines and reporting formats further hampers cross-study comparison and synthesis.
From an industrial perspective, the high capital and operational costs associated with high-resolution mass spectrometers, high-throughput sequencers, and specialized consumables represent major barriers, particularly for small and medium-sized producers [55]. In parallel, the shortage of multidisciplinary expertise and the high analytical threshold required for data interpretation impede large-scale adoption [19,57]. Collectively, these constraints highlight a critical gap between mechanistic insight and stable, reproducible technological implementation. Despite recent progress, bridging the gap between laboratory-scale analysis and industrial application remains a challenge. To address these challenges, the future roadmap outlined in Figure 5 identifies the strategic milestones required to achieve adaptive and autonomous fermentation management.

5.2. Future Directions Toward Intelligent and Adaptive Metabolomics Applications

Looking forward, metabolomics in rice wine fermentation is expected to evolve toward greater precision, efficiency, intelligence, and accessibility, driving a conceptual shift from descriptive analysis to adaptive process control [63]. At the technological level, development of advanced pretreatment materials and separation strategies—such as molecularly imprinted polymers, nanocomposite sorbents, and ion mobility-mass spectrometry coupling—will enhance enrichment of trace metabolites and resolution of structural isomers in complex matrices [57]. Miniaturization and integration of high-resolution mass spectrometry with microfluidic platforms are likely to yield compact, at-line analytical systems suitable for industrial deployment. Despite significant progress, current metabolomics-driven fermentation studies remain largely correlation-based and face challenges related to standardization and industrial transferability [119]. To address these challenges, Figure 5 outlines a future roadmap toward intelligent and adaptive rice wine fermentation, highlighting key transitions from offline analysis to standardized, closed-loop process control. This roadmap reflects a broader paradigm shift in which metabolomics evolves from an analytical tool into a core component of intelligent fermentation systems.
Beyond instrumentation, future progress will increasingly depend on system-level integration. Advances in in situ sensing, multi-channel detection, and sensor arrays linked to Internet of Things architectures will support continuous monitoring and closed-loop feedback control [48]. In parallel, automated and interpretable multi-omics data pipelines, supported by artificial intelligence, will be essential to reduce analytical barriers, improve reproducibility, and enhance model transferability across production contexts.
At the industrial scale, continued cost reduction through technological iteration and standardized reagent kits will facilitate broader adoption, particularly among small and medium-sized enterprises [70]. Establishment of unified analytical standards, data formats, and quality metrics will enable construction of metabolomics-based quality assessment and certification frameworks [16]. Close collaboration among academia, industry, and research institutions will further accelerate translation through joint development of rapid testing tools, intelligent control modules, and early-warning systems.
From a fundamental research perspective, systematic elucidation of biosynthetic pathways for novel trace metabolites and validation of key regulatory nodes through gene editing and controlled microbiome reconstruction will be critical. Extending metabolomics across the entire production chain—from raw materials to finished products—will support end-to-end quality control, standardization, and traceability. The ultimate frontier of this field lies in the integration of real-time sensing with artificial intelligence [120,121]. This future paradigm of an AI-driven digital twin system, as conceptualized in Figure 6, represents the culmination of metabolomic data integration for real-time, closed-loop control of fermentation quality [122].
Finally, metabolomics-guided design is expected to enable development of functional and customized rice wine products tailored to consumer demands for health, flavor, and sustainability. Integration with green processing technologies will further support low-risk, environmentally responsible production, aligning technological innovation with long-term industry sustainability [36].

6. Conclusions and Prospects

Metabolomics has become a powerful tool to unravel the biochemical complexity of rice wine fermentation, driving its development from empirical experience toward data-driven biomanufacturing. Technological advances have expanded metabolite coverage, reconstructed key biosynthetic pathways, and clarified the metabolic basis of flavor and quality formation. The integration of multi-omics and artificial intelligence further promotes systematic network analysis, upgrading metabolomics from conventional compositional description to an intelligent decision-support system for fermentation regulation. Nevertheless, the clarification of metabolic causality, unified analytical standardization, and practical transferability from laboratory research to industrial production still remain major unresolved challenges.
Despite these remarkable advances, the routine industrial application of metabolomics remains constrained by analytical complexity, high instrumentation costs, and a lack of unified data standardization. Overcoming these bottlenecks requires optimized enrichment strategies for trace metabolites, robust at-line analytical platforms, and rigorous causal validation supported by functional genomic evidence. Future progress will depend more on integrated systematic frameworks that couple metabolic sensing with adaptive process control. Metabolome-guided fermentation design allows targeted regulation of microbial assembly and metabolic fluxes to coordinate flavor, functional quality, and safety attributes. Extending metabolomic surveillance across the whole production chain helps improve industrial standardization and traceability. Moving forward, the synergy of high-resolution analytics, multi-omics integration, and data-driven intelligence can narrow the gap between laboratory findings and real production. Further progress still requires validated multi-site datasets, well-designed causal experiments, and scalable analytical workflows to realize reliable industrial translation in rice wine fermentation.

Author Contributions

Conceptualization, B.P. and C.L.; methodology, B.P.; validation, B.P., B.C., Z.D., J.C., L.H., L.W. and C.L.; formal analysis, B.P.; investigation, B.P., Z.D., J.C., L.H. and L.W.; resources, B.C. and C.L.; data curation, B.P. and Z.D.; writing—original draft preparation, B.P.; writing—review and editing, B.P., B.C., Z.D., J.C., L.H., L.W. and C.L.; visualization, B.P. and Z.D.; supervision, C.L.; project administration, C.L.; funding acquisition, C.L. All authors have read and agreed to the published version of the manuscript.

Funding

This study was financially supported by the Central Guidance for Local Science and Technology Development Fund Project of China (Project No. 2025CSA127), the Natural Science Foundation of Xiaogan City (Project No. XGKJ2022010116), the Open Research Project of the Research Center of Hubei Small Town Development at Hubei Engineering University (Project No. 2025K010), and the National College Student Innovation Training Program of China (Project No. S202410528014X).

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

No new data were created or analyzed in this study. Data sharing is not applicable to this article.

Acknowledgments

The authors extend their special appreciation to the Open Research Project of the Research Center of Hubei Small Town Development at Hubei Engineering University (Project No. 2025K010).

Conflicts of Interest

The authors declare no conflicts of interest.

References

  1. Peng, B.; Huang, H.; Xu, J.; Xin, Y.; Hu, L.; Wen, L.; Li, L.; Chen, J.; Han, Y.; Li, C. Rice Wine Fermentation: Unveiling Key Factors Shaping Quality, Flavor, and Technological Evolution. Foods 2025, 14, 2544. [Google Scholar] [CrossRef]
  2. Tian, S.; Zeng, W.; Zhou, J.; Du, G. Correlation between the microbial community and ethyl carbamate generated during Huzhou rice wine fermentation. Food Res. Int. 2022, 154, 111001. [Google Scholar] [CrossRef] [PubMed]
  3. Xia, Y.; Luo, H.; Wu, Z.; Zhang, W. Microbial diversity in jiuqu and its fermentation features: Saccharification, alcohol fermentation and flavors generation. Appl. Microbiol. Biotechnol. 2023, 107, 25–41. [Google Scholar] [CrossRef]
  4. Zhao, S.; Xu, Q.; Li, M.; Chen, J.; Francis, F.; Dai, X.; Tan, J.; Kong, Z. Exploring the Impact of Dinotefuran Residue on Microbial Community and Flavor Generation in Huangjiu Fermentation. J. Agric. Food Chem. 2025, 73, 17219–17232. [Google Scholar] [CrossRef]
  5. Liu, Z.; Zhang, M.; Chen, P.; Harnly, J.M.; Sun, J. Mass spectrometry-based nontargeted and targeted analytical approaches in fingerprinting and metabolomics of food and agricultural research. J. Agric. Food Chem. 2022, 70, 11138–11153. [Google Scholar] [CrossRef] [PubMed]
  6. Windarsih, A.; Rohman, A.; Riswanto, F.D.O.; Dachriyanus; Yuliana, N.D.; Bakar, N.K.A. The metabolomics approaches based on LC-MS/MS for analysis of non-halal meats in food products: A review. Agriculture 2022, 12, 984. [Google Scholar] [CrossRef]
  7. Ruan, W.; Liu, J.; Guo, H.; Yang, S.; Niu, M.; Yu, H.; Meng, X. From aroma to off-flavor: Metabolomics unveils the metabolic double-sided nature of traditional Chinese fermented foods. Food Chem. 2026, 502, 147635. [Google Scholar] [CrossRef]
  8. Wang, F.; Zhao, P.; Du, G.; Zhai, J.; Guo, Y.; Wang, X. Advancements and challenges for brewing aroma-enhancement fruit wines: Microbial metabolizing and brewing techniques. Food Chem. 2024, 456, 139981. [Google Scholar] [CrossRef]
  9. Taheri, S.; Andrade, J.C.d.; Conte-Junior, C.A. Emerging perspectives on analytical techniques and machine learning for food metabolomics in the era of industry 4.0: A systematic review. Crit. Rev. Food Sci. Nutr. 2025, 65, 6003–6029. [Google Scholar] [CrossRef] [PubMed]
  10. Kim, A.J.; Choi, J.N.; Kim, J.; Park, S.B.; Yeo, S.H.; Choi, J.H.; Lee, C.H. GC-MS based metabolite profiling of rice koji fermentation by various fungi. Biosci. Biotechnol. Biochem. 2010, 74, 2267–2272. [Google Scholar] [CrossRef]
  11. Cui, Y.; Li, Q.; Zhang, M.; Liu, Z.; Yin, W.; Liu, W.; Chen, X.; Bi, K. LC−MS determination and pharmacokinetics of p-coumaric acid in rat plasma after oral administration of p-coumaric acid and freeze-dried red wine. J. Agric. Food Chem. 2010, 58, 12083–12088. [Google Scholar] [CrossRef] [PubMed]
  12. Koda, M.; Furihata, K.; Wei, F.; Miyakawa, T.; Tanokura, M. NMR-based metabolic profiling of rice wines by F 2-selective total correlation spectra. J. Agric. Food Chem. 2012, 60, 4818–4825. [Google Scholar] [CrossRef]
  13. Diez-Simon, C.; Mumm, R.; Hall, R.D. Mass spectrometry-based metabolomics of volatiles as a new tool for understanding aroma and flavour chemistry in processed food products. Metabolomics 2019, 15, 41. [Google Scholar] [CrossRef]
  14. Zeng, X.; Fu, X.; Li, G.; Yu, S. 1H NMR analysis of rice wine treated by electric field. Guang Pu Xue Yu Guang Pu Fen Xi Guang Pu 2004, 24, 748–751. [Google Scholar]
  15. Peng, Q.; Meng, K.; Zheng, H.; Yu, H.; Zhang, Y.; Yang, X.; Lin, Z.; Xie, G. Metabolites comparison in post-fermentation stage of manual (mechanized) Chinese Huangjiu (yellow rice wine) based on GC–MS metabolomics. Food Chem. X 2022, 14, 100324. [Google Scholar] [CrossRef]
  16. Dai, C.-E.; Li, H.-L.; He, X.-P.; Zheng, F.-F.; Zhu, H.-L.; Liu, L.-F.; Du, W. Research advance in metabolism of effective ingredients from traditional Chinese medicines by probiotics. China J. Chin. Mater. Medica 2018, 43, 31–38. [Google Scholar] [CrossRef]
  17. Wang, J.; Wang, D.; Huang, M.; Sun, B.; Ren, F.; Wu, J.; Meng, N.; Zhang, J. Identification of nonvolatile chemical constituents in Chinese Huangjiu using widely targeted metabolomics. Food Res. Int. 2023, 172, 113226. [Google Scholar] [CrossRef]
  18. Pinu, F.R.; Goldansaz, S.A.; Jaine, J. Translational metabolomics: Current challenges and future opportunities. Metabolites 2019, 9, 108. [Google Scholar] [CrossRef] [PubMed]
  19. Zhang, R.; Jia, W.; Shi, L. A comprehensive review on the development of foodomics-based approaches to evaluate the quality degradation of different food products. Food Rev. Int. 2023, 39, 5563–5582. [Google Scholar] [CrossRef]
  20. Shu, N.; Chen, X.; Sun, X.; Cao, X.; Liu, Y.; Xu, Y.-J. Metabolomics identify landscape of food sensory properties. Crit. Rev. Food Sci. Nutr. 2023, 63, 8478–8488. [Google Scholar] [CrossRef]
  21. Beale, D.J.; Pinu, F.R.; Kouremenos, K.A.; Poojary, M.M.; Narayana, V.K.; Boughton, B.A.; Kanojia, K.; Dayalan, S.; Jones, O.A.; Dias, D.A. Review of recent developments in GC–MS approaches to metabolomics-based research. Metabolomics 2018, 14, 152. [Google Scholar] [CrossRef]
  22. Feng, X.; Wang, H.; Yu, Y.; Zhu, Y.; Ma, J.; Liu, Z.; Ni, L.; Lin, C.-C.; Wang, K.; Liu, Y. Exploration of the flavor diversity of oolong teas: A comprehensive analysis using metabolomics, quantification techniques, and sensory evaluation. Food Res. Int. 2024, 195, 114868. [Google Scholar] [CrossRef]
  23. Mo, X.; Fan, W.; Xu, Y. Changes in volatile compounds of Chinese rice wine wheat Qu during fermentation and storage. J. Inst. Brew. 2009, 115, 300–307. [Google Scholar] [CrossRef]
  24. Bravo, L.; Goya, L.; Lecumberri, E. LC/MS characterization of phenolic constituents of mate (Ilex paraguariensis, St. Hil.) and its antioxidant activity compared to commonly consumed beverages. Food Res. Int. 2007, 40, 393–405. [Google Scholar] [CrossRef]
  25. Chen, S.; Xu, Y. The influence of yeast strains on the volatile flavour compounds of Chinese rice wine. J. Inst. Brew. 2010, 116, 190–196. [Google Scholar] [CrossRef]
  26. Kuang, H.; Li, Z.; Peng, C.; Liu, L.; Xu, L.; Zhu, Y.; Wang, L.; Xu, C. Metabonomics approaches and the potential application in foodsafety evaluation. Crit. Rev. Food Sci. Nutr. 2012, 52, 761–774. [Google Scholar] [CrossRef]
  27. Rubert, J.; Zachariasova, M.; Hajslova, J. Advances in high-resolution mass spectrometry based on metabolomics studies for food–a review. Food Addit. Contam. Part A 2015, 32, 1685–1708. [Google Scholar] [CrossRef]
  28. Wang, N.; Chen, S.; Zhou, Z. Age-dependent characterization of volatile organic compounds and age discrimination in Chinese rice wine using an untargeted GC/MS-based metabolomic approach. Food Chem. 2020, 325, 126900. [Google Scholar] [CrossRef]
  29. Daliri, E.B.-M.; Ofosu, F.K.; Chelliah, R.; Kim, J.-H.; Kim, J.-R.; Yoo, D.; Oh, D.-H. Untargeted metabolomics of fermented rice using UHPLC Q-TOF MS/MS reveals an abundance of potential antihypertensive compounds. Foods 2020, 9, 1007. [Google Scholar] [CrossRef]
  30. Lee, G.-H.; Bang, D.-Y.; Lim, J.-H.; Yoon, S.-M.; Yea, M.-J.; Chi, Y.-M. Simultaneous determination of ethyl carbamate and urea in Korean rice wine by ultra-performance liquid chromatography coupled with mass spectrometric detection. J. Chromatogr. B 2017, 1065, 44–49. [Google Scholar] [CrossRef] [PubMed]
  31. Bai, W.; Sun, S.; Zhao, W.; Qian, M.; Liu, X.; Chen, W. Determination of ethyl carbamate (EC) by GC-MS and characterization of aroma compounds by HS-SPME-GC-MS during wine frying status in Hakka yellow rice wine. Food Anal. Methods 2017, 10, 2068–2077. [Google Scholar] [CrossRef]
  32. Alves Filho, E.G.; Silva, L.M.A.; Ribeiro, P.R.; de Brito, E.S.; Zocolo, G.J.; Souza-Leão, P.C.; Marques, A.T.; Quintela, A.L.; Larsen, F.H.; Canuto, K.M. 1H NMR and LC-MS-based metabolomic approach for evaluation of the seasonality and viticultural practices in wines from São Francisco River Valley, a Brazilian semi-arid region. Food Chem. 2019, 289, 558–567. [Google Scholar] [CrossRef]
  33. Jiang, X.; Xie, Y.; Wan, D.; Zheng, F.; Wang, J. Simultaneously detecting ethyl carbamate and its precursors in rice wine based on a pH-responsive electrochemical impedance sensor. Anal. Chim. Acta 2020, 1126, 124–132. [Google Scholar] [CrossRef] [PubMed]
  34. Yin, X.L.; Peng, Z.X.; Pan, Y.; Lv, Y.; Long, W.; Gu, H.W.; Fu, H.; She, Y. UHPLC-QTOF-MS-based untargeted metabolomic authentication of Chinese red wines according to their grape varieties. Food Res. Int. 2024, 178, 113923. [Google Scholar] [CrossRef]
  35. Ruangchaisirawet, Y.; Lorjaroenphon, Y.; Jom, K.N. Combined metabolomics and flavoromics to follow the fermentation process in sweet fermented rice (Khao-Mak). Eur. Food Res. Technol. 2024, 250, 495–509. [Google Scholar] [CrossRef]
  36. Zhang, S.; Chen, J.; Gao, F.; Su, W.; Li, T.; Wang, Y. Foodomics as a tool for evaluating food authenticity and safety from field to table: A review. Foods 2024, 14, 15. [Google Scholar] [CrossRef] [PubMed]
  37. Gao, Y.; Hou, L.; Gao, J.; Li, D.; Tian, Z.; Fan, B.; Wang, F.; Li, S. Metabolomics approaches for the comprehensive evaluation of fermented foods: A review. Foods 2021, 10, 2294. [Google Scholar] [CrossRef] [PubMed]
  38. Wang, H.; Shang, R.; Gao, S.; Huang, A.; Huang, H.; Li, W.; Guo, H. Characterization of key aroma compounds in a novel Chinese rice wine Xijiao Huojiu during its biological-ageing-like process by untargeted metabolomics. Heliyon 2024, 10, e34396. [Google Scholar] [CrossRef]
  39. Liang, Z.C.; Lin, X.Z.; He, Z.G.; Su, H.; Li, W.X.; Guo, Q.Q. Comparison of microbial communities and amino acid metabolites in different traditional fermentation starters used during the fermentation of Hong Qu glutinous rice wine. Food Res. Int. 2020, 136, 109329. [Google Scholar] [CrossRef]
  40. Shen, C.; Yu, Y.; Zhang, X.; Zhang, H.; Chu, M.; Yuan, B.; Guo, Y.; Li, Y.; Zhou, J.; Mao, J.; et al. The dynamic of physicochemical properties, volatile compounds and microbial community during the fermentation of Chinese rice wine with diverse cereals. Food Res. Int. 2024, 198, 115319. [Google Scholar] [CrossRef]
  41. Zhao, X.; Zou, H.; Du, G.; Chen, J.; Zhou, J. Effects of nitrogen catabolite repression-related amino acids on the flavour of rice wine. J. Inst. Brew. 2015, 121, 581–588. [Google Scholar] [CrossRef]
  42. Yuan, H.; Wu, Z.; Liu, H.; He, X.; Liao, Z.; Luo, W.; Li, L.; Yin, L.; Wu, F.; Zhang, L.; et al. Screening, identification, and characterization of molds for brewing rice wine: Scale-up production in a bioreactor. PLoS ONE 2024, 19, e0300213. [Google Scholar] [CrossRef]
  43. Xu, Q.; Zhao, S.; Chen, J.; Wang, R.; Wang, K.; Dai, X.; Kong, Z. Safety Risks and Quality Control in Huangjiu: From Raw Materials to Fermentation Process. Agric. Commun. 2025, 3, 100085. [Google Scholar] [CrossRef]
  44. Dai, Y.; Chen, Y.; Lin, X.; Zhang, S. Recent Applications and Prospects of Enzymes in Quality and Safety Control of Fermented Foods. Foods 2024, 13, 3804. [Google Scholar] [CrossRef]
  45. Zheng, X.; Huang, M.; Liu, J.; Yang, B.; Yu, J.; Liu, W.; Li, W.; Jing, G.; Liu, W. Quantitative determination of ethyl carbamate in alcoholic beverages using gas chromatography coupled to ion mobility spectrometer. Food Chem. 2025, 496, 146710. [Google Scholar] [CrossRef]
  46. Shi, H.; An, F.; Lin, H.; Li, M.; Wu, J.; Wu, R. Advances in fermented foods revealed by multi-omics: A new direction toward precisely clarifying the roles of microorganisms. Front. Microbiol. 2022, 13, 1044820. [Google Scholar] [CrossRef]
  47. Huang, Y. Volatile organic compounds in rice wine: Formation mechanisms and analytical approaches: Y. Huang. Food Sci. Biotechnol. 2026, 1–19. [Google Scholar] [CrossRef]
  48. Ge, X.; Zhou, Y.; Li, Q.; Tan, Y.; Luo, Y.; Hong, H. Machine learning for food flavor prediction and regulation: Models, data integration, and future perspectives. J. Adv. Res. 2025; in press. [CrossRef]
  49. Putri, S.P.; Ikram, M.M.M.; Sato, A.; Dahlan, H.A.; Rahmawati, D.; Ohto, Y.; Fukusaki, E. Application of gas chromatography-mass spectrometry-based metabolomics in food science and technology. J. Biosci. Bioeng. 2022, 133, 425–435. [Google Scholar] [CrossRef]
  50. Kharbach, M.; Alaoui Mansouri, M.; Taabouz, M.; Yu, H. Current application of advancing spectroscopy techniques in food analysis: Data handling with chemometric approaches. Foods 2023, 12, 2753. [Google Scholar] [CrossRef]
  51. Fraga-Corral, M.; Carpena, M.; Garcia-Oliveira, P.; Pereira, A.; Prieto, M.; Simal-Gandara, J. Analytical metabolomics and applications in health, environmental and food science. Crit. Rev. Anal. Chem. 2022, 52, 712–734. [Google Scholar] [CrossRef]
  52. Li, S.; Han, Y.; Yan, M.; Qiu, S.; Lu, J. Machine learning and multi-omics integration to reveal biomarkers and microbial community assembly differences in abnormal stacking fermentation of sauce-flavor baijiu. Foods 2025, 14, 245. [Google Scholar] [CrossRef]
  53. Sharma, P.; Vishwakarma, R.; Varjani, S.; Gautam, K.; Gaur, V.K.; Farooqui, A.; Sindhu, R.; Binod, P.; Awasthi, M.K.; Chaturvedi, P.; et al. Multi-omics approaches for remediation of bisphenol A: Toxicity, risk analysis, road blocks and research perspectives. Environ. Res. 2022, 215, 114198. [Google Scholar] [CrossRef]
  54. Kang, J.M.; Xue, Y.S.; Chen, X.X.; Han, B.Z. Integrated multi-omics approaches to understand microbiome assembly in Jiuqu, a mixed-culture starter. Compr. Rev. Food Sci. Food Saf. 2022, 21, 4076–4107. [Google Scholar] [CrossRef]
  55. Feng, L.; Wang, S.; Chen, H. Research progress on volatile compounds and microbial metabolism of traditional fermented soybean products in China. Food Biosci. 2024, 61, 104558. [Google Scholar] [CrossRef]
  56. He, Y.; Wang, X.; Li, P.; Lv, Y.; Nan, H.; Wen, L.; Wang, Z. Research progress of wine aroma components: A critical review. Food Chem. 2023, 402, 134491. [Google Scholar] [CrossRef]
  57. Anagho-Mattanovich, M.; Paige, H.A.; Medina, R.H.; Moritz, T. Stable-isotope labeling using mass spectrometry for metabolism research. TrAC Trends Anal. Chem. 2026, 196, 118691. [Google Scholar] [CrossRef]
  58. Zhang, S.J.; Lin, Y.; Wu, T.X.; Yuan, D.D.; Jiang, S.F.; Fu, P. Analysis of Microorganisms, Volatile and Non-Volatile Metabolites During the Fermentation Process of Gastrodia Elata Sweet Rice Wine Based on Multi-Omics Technology. J. Food Sci. 2025, 90, e70774. [Google Scholar] [CrossRef]
  59. Yamazaki, Y.; Sasaki, T.; Ochiai, N.; Sasamoto, K.; Michihata, T.; Yoshida, K.; Koyanagi, T. Mechanisms of aroma compound formation in traditional Japanese sake brewing by natural lactic acid fermentation: Insights from solvent-assisted stir bar sorptive extraction with GC-MS and bacterial flora analysis. Food Chem. 2026, 502, 147596. [Google Scholar] [CrossRef]
  60. Wang, Y.; Liang, H.; Hu, Z.; Chen, L.; Zhu, L.; Zhuang, K.; Ding, W.; Shen, Q. Evaluation of flavor properties in rice bran by solid-state fermentation with yeast. Food Chem. X 2025, 28, 102516. [Google Scholar] [CrossRef]
  61. Lei, C.; Chen, J.; Chen, Z.; Ma, C.; Chen, X.; Sun, X.; Tang, X.; Deng, J.; Wang, S.; Jiang, J.; et al. Spatial metabolomics in mental disorders and traditional Chinese medicine: A review. Front. Pharmacol. 2025, 16, 1449639. [Google Scholar] [CrossRef]
  62. Du, Z.; Sun, L.; Lin, Y.; Yang, F.; Cai, Y. Using PacBio SMRT Sequencing Technology and Metabolomics to Explore the Microbiota-Metabolome Interaction Related to Silage Fermentation of Woody Plant. Front. Microbiol. 2022, 13, 857431. [Google Scholar] [CrossRef]
  63. Zhang, J.; Li, T.; Zou, G.; Wei, Y.; Qu, L. Advancements and future directions in yellow rice wine production research. Fermentation 2024, 10, 40. [Google Scholar] [CrossRef]
  64. Tan, J.; Zhang, L.; Cen, L.; Dai, Y.; Qiu, S.; Zeng, X.; Wang, X.; Wei, C. Metabolomics-based analysis of the regulatory effects of Dendrobium nobile Lindl. on key bioactive metabolites during rice wine fermentation. J. Sci. Food Agric. 2026, 106, 1772–1784. [Google Scholar] [CrossRef]
  65. Du, Y.-H.; Ye, Y.-Q.; Hao, Z.-P.; Tan, X.-Y.; Ye, M.-Q. Research on wine flavor: A bibliometric and visual analysis (2003–2022). Food Chem. Adv. 2024, 4, 100717. [Google Scholar] [CrossRef]
  66. Mao, X.; Yue, S.-J.; Xu, D.-Q.; Fu, R.-J.; Han, J.-Z.; Zhou, H.-M.; Tang, Y.-P. Research progress on flavor and quality of Chinese rice wine in the brewing process. ACS Omega 2023, 8, 32311–32330. [Google Scholar] [CrossRef]
  67. Xiaohui, G.; Zhang, D.; Ling, X.; Chen, C. Research Progress on the Relationship between Microbial Community and Flavor Quality Formation in Rice Wine. Food Sci. 2024, 45, 358–366. [Google Scholar]
  68. Moon, H.Y.; Kim, H.J.; Kim, K.S.; Yoo, S.J.; Lee, D.W.; Shin, H.J.; Seo, J.-A.; Kang, H.A. Molecular characterization of the Saccharomycopsis fibuligera ATF genes, encoding alcohol acetyltransferase for volatile acetate ester formation. J. Microbiol. 2021, 59, 598–608. [Google Scholar] [CrossRef]
  69. Mandlaa; Ren, Y.; Qiao, M.; Yang, Y.; Ren, G.; Chen, Z.; Sun, Z. Profiling the potential producers of higher alcohol in different Daqu (a starter of Baijiu) by amplifying the key enzyme gene. Cogent Food Agric. 2024, 10, 2306014. [Google Scholar] [CrossRef]
  70. Zeng, J.; Gong, L.; Qin, S.; Fang, P.; Shu, F.; Zhang, W.; Zhou, Y.; Li, X.; He, Q.; Sun, P.; et al. Multi-omics reveals glutinous rice varieties shape Baijiu flavor via microbial and metabolic modulation. Front. Microbiol. 2025, 16, 1721127. [Google Scholar] [CrossRef]
  71. Liu, S.P.; Mao, J.; Liu, Y.Y.; Meng, X.Y.; Ji, Z.W.; Zhou, Z.L.; Ai-lati, A. Bacterial succession and the dynamics of volatile compounds during the fermentation of Chinese rice wine from Shaoxing region. World J. Microbiol. Biotechnol. 2015, 31, 1907–1921. [Google Scholar] [CrossRef]
  72. Wang, M.; Liu, Y.; Guo, X.; Ding, Y.; Liu, D. Optimizing the Brewing Process, Metabolomics Analysis, and Antioxidant Activity Analysis of Complexed Hongqu Rice Wine with Kiwiberry. Fermentation 2024, 10, 494. [Google Scholar] [CrossRef]
  73. Gong, Y.; Hu, J.; Xie, T.; Mou, H.; Xiao, S.; Yao, Z.; Yang, T. Investigation on the multi-scale structure and digestibility of starch in sweet rice wine and its vinasse: Insights from indica and glutinous rice varieties. Food Chem. X 2025, 28, 102521. [Google Scholar] [CrossRef]
  74. Jiang, L.; Su, W.; Mu, Y.; Mu, Y. Major Metabolites and Microbial Community of Fermented Black Glutinous Rice Wine With Different Starters. Front. Microbiol. 2020, 11, 593. [Google Scholar] [CrossRef]
  75. Ren, N.; Gong, W.; Zhao, Y.; Zhao, D.G.; Xu, Y. Innovation in sweet rice wine with high antioxidant activity: Eucommia ulmoides leaf sweet rice wine. Front. Nutr. 2022, 9, 1108843. [Google Scholar] [CrossRef]
  76. Yang, J.; Song, J.; Zhou, J.; Lin, H.; Wu, Z.; Liu, N.; Xie, W.; Guo, H.; Chi, J. Functional components of Chinese rice wine can ameliorate diabetic cardiomyopathy through the modulation of autophagy, apoptosis, gut microbiota, and metabolites. Front. Cardiovasc. Med. 2022, 9, 940663. [Google Scholar] [CrossRef]
  77. Kim, T.J.; Kim, Y.J.; Seo, W.D.; Park, S.U.; Kim, J.K. Improved quantification of catechin and epicatechin in red rice (Oryza sativa L.) using stable isotope dilution liquid chromatography-mass spectrometry. Appl. Biol. Chem. 2022, 65, 85. [Google Scholar] [CrossRef]
  78. Ahn, J.; Bae, S.; Lee, G.; Kim, G.D.; Son, H.S. Influence of alcohol level and temperature on rice wine metabolite changes during 200-day storage. LWT 2025, 238, 118808. [Google Scholar] [CrossRef]
  79. Huang, Y.; Lu, W.-W.; Chen, B.; Wu, M.; Li, S.-G. Determination of 13 phenolic compounds in rice wine by high-performance liquid chromatography. Food Anal. Methods 2015, 8, 825–832. [Google Scholar] [CrossRef]
  80. Liu, M.; Zhao, B.; Wang, P.; Wang, B.; Li, J.; Meng, N.; Li, H.; Sun, J.; Sun, B. The regulatory mechanism of mannan from millet Huangjiu on flavor release. Carbohydr. Polym. 2025, 348, 122808. [Google Scholar] [CrossRef]
  81. Tang, A.; Peng, B. Metatranscriptomics reveals microbial community function succession and characteristic flavor formation mechanisms during black rice wine fermentation. Food Chem. 2024, 457, 140428. [Google Scholar] [CrossRef]
  82. Meng, L.; Liu, L.; Zhou, C.; Pan, S.; Zhai, X.; Jiang, C.; Guo, Y.; Ji, Z.; Chi, J.; Peng, F.; et al. Polyphenols and Polypeptides in Chinese Rice Wine Inhibit Homocysteine-induced Proliferation and Migration of Vascular Smooth Muscle Cells. J. Cardiovasc. Pharmacol. 2016, 67, 482–490. [Google Scholar] [CrossRef]
  83. Peng, L.; Ai-Lati, A.; Ji, Z.; Chen, S.; Mao, J. Polyphenols extracted from huangjiu have anti-inflammatory activity in lipopolysaccharide stimulated RAW264.7 cells. RSC Adv. 2019, 9, 5295–5301. [Google Scholar] [CrossRef]
  84. Chen, M.Y.; Zhang, S.T.; Ren, Y.X.; Le, Z.; Li, L.X.; Sun, B.S. Effects of Different Brewing Technologies on Polyphenols and Aroma Components of Black Chokeberry Wine. Foods 2023, 12, 868. [Google Scholar] [CrossRef]
  85. Cao, C.; Liu, Y.; Xie, H.; Wan, L.; Dong, L.; Wang, J.; Han, X. The Impact of Different Concentrations of Raspberry Extract on the Ethyl Carbamate Content in Chinese Rice Wine. J. Food Sci. 2025, 90, e70461. [Google Scholar] [CrossRef]
  86. Zhu, Y.; Wang, B.; Liu, M.; Li, H.; Zhao, D.; Sun, B.; Sun, J. From fermentation to storage: Innovative development of nanosensing technology and biological regulation strategies for the full lifecycle control of ethyl carbamate. Food Control 2026, 183, 111959. [Google Scholar] [CrossRef]
  87. Yawadio, R.; Tanimori, S.; Morita, N. Identification of phenolic compounds isolated from pigmented rices and their aldose reductase inhibitory activities. Food Chem. 2007, 101, 1616–1625. [Google Scholar] [CrossRef]
  88. Gide, S.; Abdulkarim, S.; Danjuma, L. Effects of fermentation process on levels of Aflatoxin B1 in Cereals. Dutse J. Pure Appl. Sci. 2025, 11, 8–16. [Google Scholar] [CrossRef]
  89. Lee, J.G.; Park, S.K.; Yoon, H.J.; Kang, D.H.; Kim, M. Exposure assessment and risk characterisation of ethyl carbamate from Korean traditional fermented rice wine, Takju and Yakju. Food Addit. Contam. Part A 2016, 33, 207–214. [Google Scholar] [CrossRef]
  90. Fu, M.-L.; Liu, J.; Chen, Q.-H.; Liu, X.-J.; He, G.-Q.; Chen, J.-C. Determination of ethyl carbamate in Chinese yellow rice wine using high-performance liquid chromatography with fluorescence detection. Int. J. Food Sci. Technol. 2010, 45, 1297–1302. [Google Scholar] [CrossRef]
  91. Luo, Q.; Shi, R.; Gong, P.; Liu, Y.; Chen, W.; Wang, C. Biogenic amines in Huangjiu (Chinese rice wine): Formation, hazard, detection, and reduction. LWT 2022, 168, 113952. [Google Scholar] [CrossRef]
  92. Nguyen, M.T.; Tozlovanu, M.; Tran, T.L.; Pfohl-Leszkowicz, A. Occurrence of aflatoxin B1, citrinin and ochratoxin A in rice in five provinces of the central region of Vietnam. Food Chem. 2007, 105, 42–47. [Google Scholar] [CrossRef]
  93. Kan, X.; Yao, Y.; Chen, Z.; Rong, Q.; Du, F.; Liu, J.; Li, X.; Liu, X.; Yao, D. Multi-omics and quantitative technique reveal the effects of post-fermentation on the metabolites and flavor quality of sea buckthorn leaves tea. J. Future Foods, 2026; in press. [CrossRef]
  94. Zhao, Y.; Liu, J.; Wang, H.; Gou, F.; He, Y.; Yang, L. Advancements in Fermented Beverage Safety: Isolation and Application of Clavispora lusitaniae Cl-p for Ethyl Carbamate Degradation and Enhanced Flavor Profile. Microorganisms 2024, 12, 882. [Google Scholar] [CrossRef]
  95. Chen, G.-M.; Huang, Z.-R.; Wu, L.; Wu, Q.; Guo, W.-L.; Zhao, W.-H.; Liu, B.; Zhang, W.; Rao, P.-F.; Lv, X.-C.; et al. Microbial diversity and flavor of Chinese rice wine (Huangjiu): An overview of current research and future prospects. Curr. Opin. Food Sci. 2021, 42, 37–50. [Google Scholar] [CrossRef]
  96. Li, S.; Tian, Y.; Jiang, P.; Lin, Y.; Liu, X.; Yang, H. Recent advances in the application of metabolomics for food safety control and food quality analyses. Crit. Rev. Food Sci. Nutr. 2021, 61, 1448–1469. [Google Scholar] [CrossRef]
  97. Chen, G.; Li, W.; Yang, Z.; Liang, Z.; Chen, S.; Qiu, Y.; Lv, X.; Ai, L.; Ni, L. Insights into microbial communities and metabolic profiles in the traditional production of the two representative Hongqu rice wines fermented with Gutian Qu and Wuyi Qu based on single-molecule real-time sequencing. Food Res. Int. 2023, 173, 113488. [Google Scholar] [CrossRef]
  98. Wang, Z.M.; Wang, C.T.; Shen, C.H.; Wang, S.T.; Mao, J.Q.; Li, Z.; Gänzle, M.; Mao, J. Microbiota stratification and succession of amylase-producing Bacillus in traditional Chinese Jiuqu (fermentation starters). J. Sci. Food Agric. 2020, 100, 3544–3553. [Google Scholar] [CrossRef] [PubMed]
  99. Yi, X.; Xia, H.; Huang, P.; Ma, S.; Wu, C. Exploring Community Succession, Assembly Patterns, and Metabolic Functions of Ester-Producing-Related Microbiota during the Production of Nongxiangxing baijiu. Foods 2024, 13, 3169. [Google Scholar] [CrossRef]
  100. Peng, Q.; Huang, J.; Li, S.; Chen, Z.; Zhu, Q.; Yuan, H.; Li, J.; Massou, B.B.; Xie, G. Dynamics of microbial communities and metabolites during the fermentation of Ningxia goji berry wine: An integrated metagenomics and metabolomics approach. Food Res. Int. 2025, 201, 115609. [Google Scholar] [CrossRef]
  101. Luo, Y.; Zhang, C.; Liao, H.; Luo, Y.; Huang, X.; Wang, Z.; Xiaole, X. Integrative metagenomics, volatilomics and chemometrics for deciphering the microbial structure and core metabolic network during Chinese rice wine (Huangjiu) fermentation in different regions. Food Microbiol. 2024, 122, 104569. [Google Scholar] [CrossRef]
  102. Xiong, Q.; Wu, H.; Lai, D.; Peng, Y.; Zhao, X.; Yang, Z.; Zhou, D. A novel preparation method for black rice wine (beer, Huangjiu and sweet wine) and its association with a core nutrient-metabolite network. Food Chem. 2025, 492, 145585. [Google Scholar] [CrossRef] [PubMed]
  103. Wang, C.; Yuan, G.; He, Y.; Tang, J.; Zhou, H.; Qiu, S. The formation of higher alcohols in rice wine fermentation using different rice cultivars. Front. Microbiol. 2022, 13, 978323. [Google Scholar] [CrossRef]
  104. Wang, X.D.; Ban, S.D.; Qiu, S.Y. Analysis of the mould microbiome and exogenous enzyme production in Moutai-flavor Daqu. J. Inst. Brew. 2018, 124, 91–99. [Google Scholar] [CrossRef]
  105. Son, E.Y.; Lee, S.M.; Kim, M.; Seo, J.A.; Kim, Y.S. Comparison of volatile and non-volatile metabolites in rice wine fermented by Koji inoculated with Saccharomycopsis fibuligera and Aspergillus oryzae. Food Res. Int. 2018, 109, 596–605. [Google Scholar] [CrossRef] [PubMed]
  106. Cha, J.; Park, S.E.; Kim, E.J.; Seo, S.H.; Cho, K.M.; Kwon, S.J.; Lee, M.H.; Son, H.S. Effects of saccharification agents on the microbial and metabolic profiles of Korean rice wine (makgeolli). Food Res. Int. 2023, 172, 113367. [Google Scholar] [CrossRef]
  107. Deng, Y.; Zheng, H.; Zhao, M.; Cao, C.; Kan, H.; Wu, B.; Liu, Y. Flavor characteristics and metabolomics of sweet rice wine fermented with different non-Saccharomyces yeasts. Food Res. Int. 2025, 211, 116473. [Google Scholar] [CrossRef] [PubMed]
  108. Zhao, C.; Su, W.; Mu, Y.; Luo, L.; Zhao, M.; Qiu, S.; Su, G.; Jiang, L. Effects of Jiuqu inoculating Rhizopus oryzae Q303 and Saccharomyces cerevisiae on chemical components and microbiota during black glutinous rice wine fermentation. Int. J. Food Microbiol. 2023, 385, 110012. [Google Scholar] [CrossRef]
  109. Wan, B.; Tian, T.; Xiong, Y.; Wang, S.; Luo, X.; Liao, W.; Liu, P.; Miao, L.; Gao, R. Isolation and Evaluation of Rhizopus arrhizus Strains from Traditional Rice Wine Starters (Jiuqu): Enzyme Activities, Antioxidant Capacity, and Flavour Compounds. Foods 2025, 14, 312. [Google Scholar] [CrossRef]
  110. Qian, M.; Ruan, F.X.; Zhao, W.H.; Dong, H.; Bai, W.D.; Li, X.L.; Huang, X.Y.; Li, Y.X. The dynamics of physicochemical properties, microbial community, and flavor metabolites during the fermentation of semi-dry Hakka rice wine and traditional sweet rice wine. Food Chem. 2023, 416, 135844. [Google Scholar] [CrossRef]
  111. Xu, J.; Wu, H.; Wang, Z.; Zheng, F.; Lu, X.; Li, Z.; Ren, Q. Microbial dynamics and metabolite changes in Chinese Rice Wine fermentation from sorghum with different tannin content. Sci. Rep. 2018, 8, 4639. [Google Scholar] [CrossRef] [PubMed]
  112. Yang, Y.; Li, S.; Xia, Y.; Wang, G.; Ni, L.; Zhang, H.; Ai, L. Effects of different lactic acid bacteria on the characteristic flavor profiles of Chinese rice wine. J. Sci. Food Agric. 2024, 104, 421–430. [Google Scholar] [CrossRef] [PubMed]
  113. Capozzi, V.; Russo, P.; Ladero, V.; Fernández, M.; Fiocco, D.; Alvarez, M.A.; Grieco, F.; Spano, G. Biogenic amines degradation by Lactobacillus plantarum: Toward a potential application in wine. Front. Microbiol. 2012, 3, 122. [Google Scholar] [CrossRef] [PubMed]
  114. Xue, G.; Qu, Y.; Wu, D.; Huang, S.; Che, Y.; Yu, J.; Song, P. Biodegradation of aflatoxin B1 in the Baijiu brewing process by Bacillus cereus. Toxins 2023, 15, 65. [Google Scholar] [CrossRef]
  115. Sang, X.; Li, K.; Zhu, Y.; Ma, X.; Hao, H.; Bi, J.; Zhang, G.; Hou, H. The impact of microbial diversity on biogenic amines formation in grasshopper sub shrimp paste during the fermentation. Front. Microbiol. 2020, 11, 782. [Google Scholar] [CrossRef]
  116. Li, X.; Yao, J.; Lei, D.; Xiong, J.; Wang, H.; Cai, T.; Xiang, W.; Tang, J. The dynamic of biogenic amines and higher alcohols of Chinese rice wine during fermentation. Food Sci. Biotechnol. 2025, 34, 1423–1432. [Google Scholar] [CrossRef]
  117. Xia, X.; Zhang, Q.; Zhang, B.; Zhang, W.; Wang, W. Insights into the Biogenic Amine Metabolic Landscape during Industrial Semidry Chinese Rice Wine Fermentation. J. Agric. Food Chem. 2016, 64, 7385–7393. [Google Scholar] [CrossRef]
  118. Park, M.K.; Kim, Y.S. Mass spectrometry based metabolomics approach on the elucidation of volatile metabolites formation in fermented foods: A mini review. Food Sci. Biotechnol. 2021, 30, 881–890. [Google Scholar] [CrossRef]
  119. Zong, E.; Yang, J.; Zhang, J.; Wang, X.; Zhang, S.; Peng, Y.; Lai, J.; Sun, X.; Zeng, S.; Ao, L.; et al. Environmental factor driven microbial interactions regulate flavor metabolisms in polymicrobial fermented alcoholic beverages: A dynamic coupling framework. Food Res. Int. 2025, 225, 118097. [Google Scholar] [CrossRef] [PubMed]
  120. Ben Ayed, R.; Hanana, M. Artificial intelligence to improve the food and agriculture sector. J. Food Qual. 2021, 2021, 5584754. [Google Scholar] [CrossRef]
  121. Kakani, V.; Nguyen, V.H.; Kumar, B.P.; Kim, H.; Pasupuleti, V.R. A critical review on computer vision and artificial intelligence in food industry. J. Agric. Food Res. 2020, 2, 100033. [Google Scholar] [CrossRef]
  122. Ding, H.; Tian, J.; Yu, W.; Wilson, D.I.; Young, B.R.; Cui, X.; Xin, X.; Wang, Z.; Li, W. The application of artificial intelligence and big data in the food industry. Foods 2023, 12, 4511. [Google Scholar] [CrossRef]
Fermentation 12 00264 g001
Figure 1. Conceptual evolution of metabolomics in rice wine fermentation from descriptive profiling to intelligent process control. The diagram delineates the transformative trajectory of research and production paradigms across four progressive stages: (1) Empirical fermentation, representing the traditional era relying on low-resolution sensory evaluation and phenomenological observation, resulting in variable product quality; (2) Descriptive metabolomics, marking the adoption of targeted instrumental analysis for specific metabolite detection and quality monitoring; (3) Mechanism-oriented metabolomics, advancing to high-coverage untargeted profiling to elucidate metabolic pathways and regulatory networks for process optimization; and (4) Predictive and intelligent fermentation control, the future paradigm integrating real-time multi-omics, AI-driven predictive modeling, and digital twin systems to achieve dynamic, precise regulation of safety and flavor.
Figure 1. Conceptual evolution of metabolomics in rice wine fermentation from descriptive profiling to intelligent process control. The diagram delineates the transformative trajectory of research and production paradigms across four progressive stages: (1) Empirical fermentation, representing the traditional era relying on low-resolution sensory evaluation and phenomenological observation, resulting in variable product quality; (2) Descriptive metabolomics, marking the adoption of targeted instrumental analysis for specific metabolite detection and quality monitoring; (3) Mechanism-oriented metabolomics, advancing to high-coverage untargeted profiling to elucidate metabolic pathways and regulatory networks for process optimization; and (4) Predictive and intelligent fermentation control, the future paradigm integrating real-time multi-omics, AI-driven predictive modeling, and digital twin systems to achieve dynamic, precise regulation of safety and flavor.
Fermentation 12 00264 g001
Fermentation 12 00264 g002
Figure 2. Spatiotemporal resolution landscape of advanced metabolomics techniques applied in fermentation. The diagram maps analytical platforms onto a two-dimensional coordinate system defined by temporal resolution (x-axis: offline to real-time) and spatial/chemical specificity (y-axis: bulk mixture to in situ mapping). (1) Classic Zone: Represents conventional retrospective profiling (GC-MS, LC-MS, NMR) characterized by high chemical coverage but requiring invasive sampling and extensive pretreatment. (2) Fast Zone: Highlights high-throughput rapid screening tools (GC-IMS, E-nose) optimized for volatile fingerprinting with minimal preparation. (3) Spatial Frontier: Illustrates mass spectrometry imaging (MALDI-MSI, DESI-MSI) for visualizing metabolite distribution within solid matrices (e.g., rice kernels, Qu starters). (4) Ultimate Goal: Depicts real-time online strategies (e.g., DART-MS) coupled directly to fermentation vessels for continuous, in situ monitoring of metabolic flux.
Figure 2. Spatiotemporal resolution landscape of advanced metabolomics techniques applied in fermentation. The diagram maps analytical platforms onto a two-dimensional coordinate system defined by temporal resolution (x-axis: offline to real-time) and spatial/chemical specificity (y-axis: bulk mixture to in situ mapping). (1) Classic Zone: Represents conventional retrospective profiling (GC-MS, LC-MS, NMR) characterized by high chemical coverage but requiring invasive sampling and extensive pretreatment. (2) Fast Zone: Highlights high-throughput rapid screening tools (GC-IMS, E-nose) optimized for volatile fingerprinting with minimal preparation. (3) Spatial Frontier: Illustrates mass spectrometry imaging (MALDI-MSI, DESI-MSI) for visualizing metabolite distribution within solid matrices (e.g., rice kernels, Qu starters). (4) Ultimate Goal: Depicts real-time online strategies (e.g., DART-MS) coupled directly to fermentation vessels for continuous, in situ monitoring of metabolic flux.
Fermentation 12 00264 g002
Fermentation 12 00264 g003
Figure 3. An integrative metabolic network illustrating how Jiuqu-associated microorganisms drive substrate transformation, flavor precursor generation, and product differentiation among rice wines. This diagram links rice substrates, Jiuqu microorganisms, core metabolic pathways, and final rice wine types. Rhizopus, Aspergillus, yeasts, and lactic acid bacteria mediate starch degradation, glycolysis, TCA cycling, amino acid, lipid, organic acid, and ester metabolism. These processes generate ethanol, organic acids, esters, higher alcohols, amino acids, and polysaccharides, shaping the distinct sensory profiles of Huangjiu, Sweet Rice Wine, and Red Rice Wine.
Figure 3. An integrative metabolic network illustrating how Jiuqu-associated microorganisms drive substrate transformation, flavor precursor generation, and product differentiation among rice wines. This diagram links rice substrates, Jiuqu microorganisms, core metabolic pathways, and final rice wine types. Rhizopus, Aspergillus, yeasts, and lactic acid bacteria mediate starch degradation, glycolysis, TCA cycling, amino acid, lipid, organic acid, and ester metabolism. These processes generate ethanol, organic acids, esters, higher alcohols, amino acids, and polysaccharides, shaping the distinct sensory profiles of Huangjiu, Sweet Rice Wine, and Red Rice Wine.
Fermentation 12 00264 g003
Fermentation 12 00264 g004
Figure 4. Multi-omics-driven regulatory framework underlying rice wine fermentation. The schematic illustrates the systems-level data integration strategy linking biological layers to fermentation phenotypes. (1) Biological Hierarchy: The fermentation process is conceptualized as a cascade starting from Raw Materials (Substrate/Starter), progressing through the Microbial Community (analyzed via Metagenomics) and Gene Expression (Transcriptomics), to the functional Metabolome (Metabolomics). This biological flux ultimately determines the Fermentation Phenotypes, encompassing Flavor, Functionality, and Safety profiles. (2) Computational Feedback Loop: High-dimensional data from each omics layer are ingested into an Integrated AI Layer. Here, advanced algorithms (Data Analytics & AI Modeling) perform predictive analysis to identify key regulatory nodes. The output drives a Decision Support & Control system, providing actionable feedback (e.g., process optimization) to the raw material or fermentation stages, thereby closing the loop for precision regulation.
Figure 4. Multi-omics-driven regulatory framework underlying rice wine fermentation. The schematic illustrates the systems-level data integration strategy linking biological layers to fermentation phenotypes. (1) Biological Hierarchy: The fermentation process is conceptualized as a cascade starting from Raw Materials (Substrate/Starter), progressing through the Microbial Community (analyzed via Metagenomics) and Gene Expression (Transcriptomics), to the functional Metabolome (Metabolomics). This biological flux ultimately determines the Fermentation Phenotypes, encompassing Flavor, Functionality, and Safety profiles. (2) Computational Feedback Loop: High-dimensional data from each omics layer are ingested into an Integrated AI Layer. Here, advanced algorithms (Data Analytics & AI Modeling) perform predictive analysis to identify key regulatory nodes. The output drives a Decision Support & Control system, providing actionable feedback (e.g., process optimization) to the raw material or fermentation stages, thereby closing the loop for precision regulation.
Fermentation 12 00264 g004
Fermentation 12 00264 g005
Figure 5. Future roadmap toward intelligent and adaptive rice wine fermentation. The diagram outlines the strategic trajectory for translating metabolomics from academic research to industrial application across three developmental phases. (1) Current State: Characterized by foundational research relying on offline, time-delayed data collection, correlation-based association studies, and laboratory-scale experimentation. (2) Near Future: Represents the transitional phase of validation and piloting, emphasizing the shift toward at-line monitoring (near-real-time), causality validation to confirm metabolic drivers, and pilot-scale implementation for process scaling. (3) Long-term Vision: Targets the realization of intelligent industrial systems, featuring closed-loop adaptive control powered by self-optimizing AI, standardized reproducible workflows, and fully automated industrial-scale fermentation.
Figure 5. Future roadmap toward intelligent and adaptive rice wine fermentation. The diagram outlines the strategic trajectory for translating metabolomics from academic research to industrial application across three developmental phases. (1) Current State: Characterized by foundational research relying on offline, time-delayed data collection, correlation-based association studies, and laboratory-scale experimentation. (2) Near Future: Represents the transitional phase of validation and piloting, emphasizing the shift toward at-line monitoring (near-real-time), causality validation to confirm metabolic drivers, and pilot-scale implementation for process scaling. (3) Long-term Vision: Targets the realization of intelligent industrial systems, featuring closed-loop adaptive control powered by self-optimizing AI, standardized reproducible workflows, and fully automated industrial-scale fermentation.
Fermentation 12 00264 g005
Fermentation 12 00264 g006
Figure 6. Future paradigm of AI-driven digital twin system for adaptive control of rice wine fermentation. The schematic illustrates a closed-loop Cyber-Physical System (CPS) designed for autonomous process optimization. (Left) Physical Space: Represents the actual fermentation environment equipped with multi-modal sensor arrays (e.g., NIR spectroscopy, E-nose, pH/DO probes) for real-time, high-frequency data acquisition. (Right) Cyber Space: Acts as the computational “brain” or digital mirror, where heterogeneous data streams (spectral fingerprints, olfactory signals, physicochemical indices) undergo Data Fusion and are processed via Artificial Neural Networks (ANN). (Action) Feedback Loop: The AI model predicts evolving flavor profiles and generates instantaneous control commands (Auto-Feedback) to dynamically adjust critical parameters (e.g., temperature, nutrient feeding) in the physical tank, ensuring precise alignment with target quality standards.
Figure 6. Future paradigm of AI-driven digital twin system for adaptive control of rice wine fermentation. The schematic illustrates a closed-loop Cyber-Physical System (CPS) designed for autonomous process optimization. (Left) Physical Space: Represents the actual fermentation environment equipped with multi-modal sensor arrays (e.g., NIR spectroscopy, E-nose, pH/DO probes) for real-time, high-frequency data acquisition. (Right) Cyber Space: Acts as the computational “brain” or digital mirror, where heterogeneous data streams (spectral fingerprints, olfactory signals, physicochemical indices) undergo Data Fusion and are processed via Artificial Neural Networks (ANN). (Action) Feedback Loop: The AI model predicts evolving flavor profiles and generates instantaneous control commands (Auto-Feedback) to dynamically adjust critical parameters (e.g., temperature, nutrient feeding) in the physical tank, ensuring precise alignment with target quality standards.
Fermentation 12 00264 g006
Table 1. Comparative evaluation of analytical performance and application scopes of mainstream and emerging metabolomics platforms in fermentation science.
Table 1. Comparative evaluation of analytical performance and application scopes of mainstream and emerging metabolomics platforms in fermentation science.
Analytical PlatformTarget AnalytesSample PretreatmentSensitivity/LODThroughput (Speed)Current LimitationApplication in Rice WineReference
GC-MS (Gas Chromatography-MS)Volatile compounds (Esters, Alcohols, Aldehydes)LLE, SPME, or SAFE (Required)High (ppm to ppb level)Low (30–60 min/sample)Limited to thermally stable volatiles; lengthy prep risks artifact formation.Standard profiling of aroma compounds; quantification of fusel oils.[59]
LC-MS/LC-MS/MS (Liquid Chromatography-MS)Non-volatile compounds (Amino acids, Peptides, Phenols)Extraction, Filtration, Derivatization (Optional)Very High (ppb to ppt level)Medium (15–30 min/sample)Matrix effect (ion suppression); complex data processing for untargeted runs.Profiling of taste-active peptides (umami) and functional polyphenols.[6]
NMR (1H Nuclear Magnetic Resonance)All abundant organic compounds (holistic overview)Minimal (Buffer addition)Low (ppm level,) μM rangeHigh (1–10 min/sample)Low sensitivity for trace flavor compounds; signal overlap in complex mixtures.Quality control consistency; absolute quantification of ethanol/sugars without standards.[32]
GC-IMS (Ion Mobility Spectrometry)Trace Volatiles & Isomers (Fingerprinting)None (Headspace injection)Ultra-High (ppb to ppt level)High (5–15 min/sample)Lack of standardized spectral libraries compared to NIST (MS); non-linear dynamic range.Rapid discrimination of vintage/age; identifying off-flavors at sub-threshold levels.[60]
MALDI-MSI/DESI-MSI (Mass Spectrometry Imaging)Spatial distribution of metabolitesMatrix application/Cryo-sectioningHigh (dependent on matrix)Variable (Hours per image)Difficult absolute quantification; surface roughness affects signal stability.In situ visualization of metabolite diffusion in Qu (starters) or rice kernels.[61]
Real-time MS (DART-MS/PTR-MS)Volatile evolution in real-timeNone (Direct ambient ionization)High (ppb level)Ultra-High (Seconds/sample)inability to separate isomers (no chromatography); mainly qualitative monitoring.Online monitoring of fermentation kinetics; rapid screening of raw materials.[62]
Note: Abbreviations: LOD, limit of detection; SPME, solid-phase microextraction; MSI, mass spectrometry imaging.
Table 3. Representative applications of metabolomics in rice wine fermentation and their practical implications.
Table 3. Representative applications of metabolomics in rice wine fermentation and their practical implications.
Application FocusMetabolomics ApproachKey InsightsPractical RelevanceReference
Flavor FormationVolatile compound profiling; Pathway mappingLinks microbial succession to sensory compound dynamicsGuides strain selection and fermentation tuning for sensory quality enhancement[7]
Functional MetabolitesBioactivity-correlated metabolite screeningIdentifies health-modulating compounds driven by enzymatic transformationsSupports development of value-added products with validated bioactive properties[76]
Safety Risk ControlToxin-targeted monitoring; Stability trackingMaps contamination pathways and degradation kineticsEnables hazard prediction systems and critical control point interventions[43]
Process Monitoring & OptimizationReal-time metabolic flux analysisReveals rate-limiting reactions and metabolic bottlenecksFacilitates adaptive process control for yield improvement and resource efficiency[97]
Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.

Share and Cite

MDPI and ACS Style

Peng, B.; Chen, B.; Dai, Z.; Chen, J.; Hu, L.; Wen, L.; Li, C. Metabolomics-Driven Insights into Rice Wine Fermentation: From Descriptive Profiling to Intelligent Process Control. Fermentation 2026, 12, 264. https://doi.org/10.3390/fermentation12060264

AMA Style

Peng B, Chen B, Dai Z, Chen J, Hu L, Wen L, Li C. Metabolomics-Driven Insights into Rice Wine Fermentation: From Descriptive Profiling to Intelligent Process Control. Fermentation. 2026; 12(6):264. https://doi.org/10.3390/fermentation12060264

Chicago/Turabian Style

Peng, Baoyu, Bifeng Chen, Zhaozhao Dai, Jinwen Chen, Lang Hu, Lelei Wen, and Changchun Li. 2026. "Metabolomics-Driven Insights into Rice Wine Fermentation: From Descriptive Profiling to Intelligent Process Control" Fermentation 12, no. 6: 264. https://doi.org/10.3390/fermentation12060264

APA Style

Peng, B., Chen, B., Dai, Z., Chen, J., Hu, L., Wen, L., & Li, C. (2026). Metabolomics-Driven Insights into Rice Wine Fermentation: From Descriptive Profiling to Intelligent Process Control. Fermentation, 12(6), 264. https://doi.org/10.3390/fermentation12060264

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

Article Metrics

Back to TopTop