Inactivation of Escherichia coli and Staphyloccocus aureus in Litchi Juice by Dimethyl Dicarbonate (DMDC) Combined With Nisin
Why this work is in the frame
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Bibliographic record
Abstract
<p>Inactivation of Gram-negative <em>Escherichia coli</em> and Gram-positive <em>Staphyloccocus aureus</em> in litchi juice by DMDC combined with nisin was individually investigated. A 1.66 log cycles reduction of<em> E. coli </em>and 2.03 log cycles reduction of <em>S. aureus </em>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 <em>E. coli </em>and <em>S. aureus</em> 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 <em>E. coli</em> and <em>S. aureus</em> 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 <em>E. coli</em> and <em>S. aureus</em> in litchi juice, and the inactivation of<em> E. coli</em> and <em>S. aureus</em> 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, <em>E. coli</em> showed higher resistance to nisin as comparing with <em>S. aureus</em>. After <em>E. coli</em> and <em>S. aureus</em> 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 <em>S. aureus</em> (6.59 log cycles) was achieved, but only 3.52 log cycles reduction of <em>E. coli</em> was observed.</p>
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it