Effects of an inoculation dose of <scp><i>Issatchenkia terricola</i> WJL‐G4</scp> on physicochemical properties, active substances, and antioxidant capacity of black, red, and white currant juice
Bibliographic record
Abstract
BACKGROUND: Due to the high level of organic acids - primarily citric acid - black, red, and white currants have an excessively sour taste, making taste adjustment during processing challenging. This study investigated and evaluated the effects of an inoculation dose of the acid-reducing yeast Issatchenkia terricola WJL-G4 on several aspect such as physicochemical properties, chromaticity, active substances, and antioxidant capacity. A sensory evaluation was also conducted. RESULTS: The results indicated that, when the inoculation dose increased from 2% to 12%, the total phenol, total flavonoid, and total anthocyanin content, and antioxidant capacity in currant juice decreased. A low inoculation dose (2-4%) was beneficial for preserving the total phenol and total flavonoid content. Although the levels of most phenolic compounds decreased, the concentrations of caffeic acid, p-coumaric acid, ferulic acid, rutin, and epicatechin were significantly higher than the control after fermentation. Overall acceptability and taste scores of fermented currants improved compared with those of the control group. CONCLUSION: This experiment provided an effective solution, with a theoretical basis, to the problems of the sour taste and harsh flavor of currant juice. © 2024 Society of Chemical 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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".