Wenjing Tian

Baijiu aroma arises from the combined effects of diverse volatile and semivolatile trace components within the Baijiu matrix. A single concentration value or odor activity value (OAV) cannot fully characterize the relative importance of a compound in a specific sample. A knowledge base containing 5572 Baijiu aroma-related compounds was used to develop a comprehensive contribution ranking model and a sample-level intensity prediction model. Odor evidence, molecular structure, physicochemical properties, volatility and partitioning characteristics, matrix information, and evidence quality were integrated. Comprehensive contribution is defined here as a within-sample relative ranking index obtained from multi-source molecular features, concentration information, and matrix conditions under a specified model setting. It is not a direct measurement of the true sensory contribution of a compound. After data standardization and screening for feature computability, 5568 compounds were included in the scorer. The R², RMSE, and MAE obtained from 9000 records in the formal independent test set were 0.952018, 0.045684, and 0.036464, respectively. Light-aroma, strong-aroma, and sauce-aroma Baijiu were then analyzed by GC×GC–TOFMS. A total of 732 trace components were obtained, of which 191, 474, and 510 were detected in the three sample groups, respectively. The aroma-related compounds in each sample group were ranked within the group. The top 20 compounds were selected and recombined at their semiquantitative concentrations in the original Baijiu. Sensory evaluation by 10 assessors showed overall similarity scores of 8.70 ± 1.16 and 9.20 ± 1.03 for the light-aroma and strong-aroma recombination samples, respectively. The correlation coefficients between their 10-dimensional sensory profiles and the mean profiles of the original samples were 0.988 and 0.992. The overall similarity score of the sauce-aroma recombination sample was 5.40 ± 1.26. Significant proportional deviations were observed among acidic aroma, sauce-like and roasted aroma, and floral and fruity aroma (p < 0.05). These results indicate that multi-source feature fusion can provide an interpretable basis for the relative screening and experimental prioritization of candidate aroma compounds in Baijiu. The rankings for the light-aroma and strong-aroma samples received strong sensory support. However, they should not be interpreted as absolute contributions across aroma types. Evaluation of sauce-aroma Baijiu requires further incorporation of aroma-type-specific prior information, low-OAV components, component interactions, and effects of the authentic Baijiu matrix.

Despite increasing interest in beer-derived umami peptides, their formation during lager beer fermentation and their potential roles in receptor recognition and sensory perception remain insufficiently understood. Therefore, this study combined peptidomics, in silico screening, receptor-oriented analysis, and sensory evaluation to investigate the dynamic changes in umami-related peptides throughout lager beer fermentation and to explore their possible sensory relevance. Peptide profiles varied markedly across the fermentation process, indicating continuous proteolysis and dynamic restructuring of the peptide pool. From these profiles, candidate umami peptides were screened, and representative short peptides with plausible receptor-binding characteristics were selected for further evaluation. These peptides showed measurable taste activity and were mainly associated with modulation of the umami–bitterness–aftertaste dimension, whereas their effects on aroma and overall balance were limited. In addition, the abundance of umami-related peptides was positively associated with perceived umami intensity. Overall, the results suggest that fermentation-derived peptides may contribute to taste perception in lager beer and provide a basis for further studies on flavor-oriented brewing and process regulation.

This study aimed to identify umami peptides in non-alcoholic beer and clarify their potential contribution to taste reconstruction and aftertaste improvement. Peptides were profiled by RPLC-Q-TOF-MS and screened using machine learning prediction, molecular docking, MM-GBSA analysis, and sensory validation. Under the criteria of −10logP ≥ 15 and ALC ≥ 90.00%, 2081 peptides were identified. Among them, 122 potential umami peptides were predicted, and 117 peptides were successfully docked with the T1R1/T1R3 umami receptor. The docked peptides were mainly short to medium oligopeptides, especially tetrapeptides and pentapeptides, which accounted for 40.17% and 35.90%, respectively. Based on docking score, structural diversity, and peptide length distribution, CTGAA, IDQILG, KDTHP, QRQ, and EITGR were selected as representative candidates. These peptides showed favorable receptor binding, mainly supported by hydrogen bonding, electrostatic interactions, and local hydrophobic contacts. Sensory validation further showed that the 5 peptides improved umami and aftertaste cleanliness to different degrees. Umami intensity increased by 7.58% to 22.73%, while aftertaste cleanliness increased by 5.80% to 17.39%. Among them, CTGAA showed the strongest umami enhancement, and QRQ produced the greatest improvement in aftertaste cleanliness. These results suggest that selected umami peptides may contribute to flavor reconstruction in non-alcoholic beer by enhancing umami perception and improving aftertaste quality.

Umami taste in lager beer not only determined body fullness and the backbone of aftertaste, but also affected the controllability and interpretability of flavor expression across the entire brewing process. Based on stage-wise sampling, peptidomic profiles were established on wort fermentation day 0, day 1, day 3, and day 9. A total of 25,592 peptides were identified by reversed-phase liquid chromatography–quadrupole time-of-flight mass spectrometry (RPLC-QTOF-MS). Molecular informatics screening was performed using UMPred-FRL (a feature representation learning-based meta-predictor for umami peptides) and TastePeptides-Meta (a one-stop platform for taste peptides and prediction models), yielding 7255 potential umami peptides. From these, 145 peptides were further selected for molecular docking. In addition, 6 representative umami peptides were selected for receptor-level validation and structural analysis. Mechanistically, the umami receptor taste receptor type 1 member 1/taste receptor type 1 member 3 (T1R1/T1R3) belonged to class C G protein-coupled receptor (GPCR) and relied on the extracellular Venus flytrap (VFT) domain for ligand capture. Ligand-induced VFT conformational convergence transmitted changes to the transmembrane region and triggered signal transduction. Docking and energy decomposition indicated that the ionic group primarily contributed to orientation and anchoring. Salt-bridge or hydrogen-bond networks were formed around Lys228, Arg240, Glu206, Asp210, Asn141, and Gln138, thereby reducing conformational freedom. Meanwhile, hydrophobic side chains obtained major binding gains within a hydrophobic microenvironment formed by Val135, Ile137, Leu165, Tyr166, Trp78, and His79. These results reflected a synergistic mode in which charge pairing enabled positioning and hydro-phobic complementarity promoted VFT closure. To experimentally confirm sensory relevance, 6 representative peptides were individually spiked into 4 brewing-stage beer samples, which produced a clear stratification pattern across stages. Notably, peptides with favorable docking-derived binding propensity did not necessarily enhance umami perception, and several longer peptides showed persistent negative sensory shifts, supporting that binding affinity alone could not be treated as a proxy for perceived umami in the beer matrix. At the node level, the cumulative abundance of umami peptides showed a significant positive correlation with umami scores, with a Pearson correlation coefficient of r = 0.963 and p = 0.037. This result indicated good linear consistency between umami peptide content and the upward shift in umami taste in lager beer. Umami peptide clusters were further proposed as a more appropriate functional unit, and an umami peptide cluster database spanning the full process was constructed. This database provided a reusable resource for process control and flavor prediction.

The storage process of Baijiu is an integral part of its production (the quality undergoes substantial changes during the aging process of Baijiu). As the storage time extends, the flavor compounds in Baijiu tend to undergo coordinated transformation, thereby enhancing the quality of Baijiu. Among them, long-chain fatty acid ethyl esters (LCFAEEs) were widely distributed in Baijiu and have been shown to have potential contributions to the quality of Baijiu. However, the current research on LCFAEEs in Baijiu predominantly focuses on the olfactory sensation aspect, while there is a lack of systematic investigation into their influence on taste and evaluation after drinking Baijiu during the aging process. In light of this, the present study investigates the distribution of LCFAEEs in Baijiu over different years. We have combined modern flavor sensory analysis with multivariate chemometrics to comprehensively and objectively explore the influence of LCFAEEs on Baijiu quality. The results demonstrate a significant positive correlation between the concentration of LCFAEEs and the fruity aroma (p < 0.05, r = 0.755) as well as the aged aroma (p < 0.05, r = 0.833) of Baijiu within a specific range; they can effectively reduce the off-flavors and spicy sensation of Baijiu. Furthermore, additional experiments utilizing a single variable suggest that LCFAEEs were crucial factors influencing the flavor of Baijiu, with Ethyl Palmitate (EP) being the most notable LCFAEE that merits further systematic investigation.

Baijiu occupies an important position in the food industry of China and is deeply recognized as the national liquor of China. According to the flavor characteristics, Baijiu is artificially divided into 12 categories. The sesame flavor of Baijiu was accidentally discovered after the founding of the People’s Republic of China. Sesame flavor Baijiu is known for its special aroma of roasted sesame, which attracts people’s attention. Modern flavor extraction, separation technology, and flavor analysis technology, greatly promote the identification and evaluation of trace components and aroma compounds in Baijiu. Of note, it has successfully identified which aroma compounds are responsible for the special roasted sesame aroma in sesame flavor Baijiu. On this basis, this paper summarizes the extraction methods, detection techniques, analysis methods, aroma expression, and sensory evaluation methods that have been applied for the verification and evaluation of trace components and aroma compounds in Baijiu. More specifically, the research progress on the revelation of aroma compounds in sesame flavor Baijiu is systematically summarized. Next, people will focus on the changing mechanisms of aroma compounds and the metabolic regulation in Baijiu during brewing, which will be helpful for industrialization and the modern production of Baijiu.