Red cabbage washing with acidic electrolysed water: effects on microbial quality and physicochemical properties
Bibliographic record
Abstract
The effects of acidic electrolysed water (AEW) as ‘green’ technology on the microbiological and physicochemical properties of fresh-cut red cabbages were studied. Fresh-cut red cabbages and artificially inoculated red cabbages with Salmonella typhimurium DT104 were washed with distilled water (DW) and different available chlorine concentrations (ACC) of AEW for different times. AEW treatments significantly reduced the populations of native aerobic bacteria, molds, and yeasts, and artificially inoculated S. typhimurium DT104 compared with the DW-treated and untreated red cabbage samples. The effectiveness of AEW treatments was greatly enhanced with increasing ACC and treatment times. S. typhimurium DT104 were not detected in the washing water that were collected after the red cabbages treated by AEW. The surface colour, pH, and total phenolic contents did not significantly change when the red cabbages were washed with DW and AEW containing 100 mg/L available chlorine for 3 min. The anthocyanin contents and antioxidant activities of red cabbage were significantly reduced by 18.5 per cent for cyanidin, 22.1 per cent for pelargonidin, and 11.2 per cent for 2,2-diphenyl-1-picrylhydrazyl (DPPH) radical scavenging activity, however, the impacts on the nutritional benefits of red cabbage were considered as limited and acceptable. The optimal process condition of AEW for washing red cabbage was 100 mg/L ACC for 3 min. In these conditions, most of the native microflora were inactivated, and artificially inoculated S. typhimurium DT104 on the red cabbage were reduced by 40.2 per cent [3.67 log CFU/g (log10 colony-forming units per gram)] and with minimal losses of nutrients and antioxidant activity, as well as no requirement of decontamination treatment on the washing water after AEW treatment.
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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 | 0.000 |
| 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".