Changes in Quality Attributes During Storage of Litchi Juice Treated With Dimethyl Dicarbonate (DMDC) and Nisin
Bibliographic record
Abstract
The aim of this work was to evaluate the change in the quality of litchi juice treated by DMDC combined with Nisin during storage of 4 °C. Results found that addition of 250 mg/L of DMDC combined with 100 IU/mL of Nisin can ensure the microbiological safe of litchi juice during storage at 4 °C. Compare with heat treatment (95°C, 1 min), the treatment of DMDC combined with Nisin can retain a more value of sensory attributes, but a more loss in the content of total phenolics, ascorbic acid, and antioxidant capacity was observed during storage at 4 °C because of the ineffectiveness of DMDC and Nisin to the oxidase of litchi juice. Moreover, no significant change (P > 0.05) was observed in the value of L∗, a∗, b∗, and △E in the heat-treated litchi juice, and yet the litchi juice treated by DMDC and Nisin gradually turned into light red at the end of storage because of the oxidation of phenolics by residual POD in the litchi juice, which resulted in a significant changes (P < 0.05) in the value of L∗, a∗, b∗, and △E in the litchi juice. This study would provide technical support for commercial application of DMDC combined with Nisin in litchi juice processing.
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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) | 0.001 | 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".