Inactivation of Escherichia coli and Staphyloccocus aureus in Litchi Juice by Dimethyl Dicarbonate (DMDC) Combined With Nisin
Bibliographic record
Abstract
Inactivation of Gram-negative Escherichia coli and Gram-positive Staphyloccocus aureus in litchi juice by DMDC combined with nisin was individually investigated. A 1.66 log cycles reduction of E. coli and 2.03 log cycles reduction of S. aureus in litchi juice (pH 4.5) added without nisin was achieved as exposed to 150 mg/l DMDC at 30 °C for 1 h, and the inactivation rate of E. coli and S. aureus during initial 1 h was far greater than during the remaining 5 h. As exposed to 150 mg/l DMDC at 30 °C for 1 h, the inactivation of E. coli and S. aureus in the litchi juice showed a trend toward increase with increasing of nisin addition level in the range from 0 to 200 IU/ml. Moreover, DMDC and nisin exhibited a synergistic effect on the inactivation of E. coli and S. aureus in litchi juice, and the inactivation of E. coli and S. aureus in the litchi juice also depends on the temperature of litchi juice, pH value of litchi juice and DMDC concentration when treated with DMDC and nisin. In addition, E. coli showed higher resistance to nisin as comparing with S. aureus. After E. coli and S. aureus in the litchi juice of pH 4.0 were individually treated with 150 mg/l DMDC combined with 200 IU/ml nisin at 30 °C for 1 h, a complete inactivation of S. aureus (6.59 log cycles) was achieved, but only 3.52 log cycles reduction of E. coli was observed.
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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 | 0.000 | 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".