The Effect of Fermentation on the Physicochemical Characteristics of Dry-Salted Vegetables
Bibliographic record
Abstract
The local food movement has prompted interest in fermented vegetables, especially novel combinations of vegetables. The physicochemical characteristics of dry-salted (2% Sodium Chloride [NaCl]) fermented carrots, daikon radish, red cabbage, carrot + daikon radish (mix I), and red cabbage + daikon radish (mix II) were examined during a 14-day fermentation at room temperature. The fermentation process was monitored through the measurement of pH, titratable acidity, and Brixvalue. Further, effects of fermentation on shredded vegetables were determined by measuring color, water activity, and texture (hardness). During the fermentation process, pH of fermented carrot, daikon radish, red cabbage, and mix I and mix II decreased significantly (p < 0.05) to 3.99±0.04, 4.17±0.05, 3.76±0.11, 3.74±0.18, and 3.70±0.05, respectively, at the end of fermentation (10 days for carrot and 14 days for other vegetables). Titratable acidity (% as lactic acid) in fermented carrot, daikon radish, red cabbage, and mix I and mix II increased throughout fermentation, and final fermentation day acidity values were 1.39±0.12, 0.78±0.02, 1.54±0.09, 1.2±0.06, and 1.50±0.07%, respectively. In general, fermentation did not impact significantly color, water activity, hardness values of fermented vegetables. The use of the dry-salting method has applications in other vegetables besides cabbage. The study results support the use of this method for other vegetables and also might be useful to provide knowledge helpful in the local food movement.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".