Optimization of Traditional Vinegar Brewing Processes Based on Natural Raw Materials and Analysis of Functional Components
Bibliographic record
Abstract
This study explored the intangible cultural heritage brewing technique of Yuyue orange vinegar. Using local tangerines as raw materials, through the organic combination of traditional fermentation techniques and modern biotechnology, the dual improvement of nutrition and functional components was achieved. The fermentation efficiency was enhanced through the targeted selection and breeding of strains. The use of acid-resistant acetic acid bacteria and yeast complex bacterial communities improved the synthesis efficiency of organic acids (such as acetic acid and citric acid) and amino acids. Meanwhile, lactic acid bacteria are introduced to promote the dissolution of polyphenols from orange peel and enhance the antioxidant activity of the product. The advancement of technology has enabled the active components such as ligustrazine in orange peels to be fully released, regulating post-meal blood sugar, having anti-inflammatory effects and potential cardiovascular protective functions, thus breaking away from the single flavoring attribute of traditional vinegar. This study also explored the synergistic mechanism between the microbial interaction network and active ingredients. The upgrade of traditional fermentation products to nutritional functional products has promoted the efficient utilization of agricultural resources and the sustainable development of the industry.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 source (direct Gemma or distilled Codex), 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".