Improving the taste and aromatic characteristics of citrus wine by co‐fermentation involving <scp> <i>Saccharomyces cerevisiae</i> </scp> and lactic acid bacteria
Bibliographic record
Abstract
BACKGROUND: Citrus wine, a value-added product of citrus deep processing, often exhibits unwanted bitterness caused by naringin and limonin, along with an aroma, which limits consumer acceptance. This study investigated the potential of co-fermenting Saccharomyces cerevisiae with selected lactic acid bacteria (LAB) to reduce bitterness and enhance aromatic quality. RESULTS: Co-fermentation of Lactiplantibacillus plantarum and Saccharomyces cerevisiae increased α-rhamnosidase activity and reduced naringin concentration from 73.87 ± 0.15 to 39.21 ± 1.62 mg L⁻¹ (P < 0.01), limiting limonin accumulation to 15.32 ± 1.01 mg L⁻¹. This was a 50.79% reduction compared with fermentation using S. cerevisiae alone. A total of 150 volatile compounds were identified by headspace solid-phasemicroextraction coupled with gas chromatography-mass spectrometry (HS-SPME-GC-MS) among which the esters, including ethyl caproate, ethyl 9-decenoate and ethyl laurate were enhanced by co-fermentation. The process also increased the odor activity value (OAV) of isoamyl acetate, isoamyl formate, octanoic acid-2-phenylethanol ester, and nonanal, which contribute to fruity and floral aroma characteristics. Sensory evaluation confirmed reduced bitterness and astringency and improved overall flavor acceptance. CONCLUSION: Co-fermentation of S. cerevisiae and L. plantarum improved citrus wine quality by degrading naringin and limonin to reduce bitterness, enriching key esters to improve aroma, achieving overall sensory optimization. This study provides a scientific basis for microbial synergy in fruit wine production, offering a practical approach to improving the organoleptic properties of citrus wine. © 2025 Society of Chemical Industry.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".