Dipping fresh‐cut apples in citric acid before plasma‐integrated low‐pressure cooling improves <i>Salmonella</i> and polyphenol oxidase inactivation
Bibliographic record
Abstract
Abstract BACKGROUND Ready‐to‐eat fruit and vegetable products have gained tremendous popularity in recent years. The main challenges associated with these minimally processed products are their short shelf life and high food safety concerns. In this study, our goal was to develop an integrated process to both reduce the Salmonella population by >5 log CFU g −1 ) and to reduce polyphenol oxidase activity, followed by quickly cooling the product. We compared the effect of a sequential treatment of dipping in citric acid (CA) followed by cold plasma (CP) treatment on the inactivation of Salmonella Typhimurium, polyphenol oxidase (PPO) activity, browning, total phenolic content and the moisture loss of cut apples during the plasma‐integrated low‐pressure cooling (PiLPC) process. RESULTS The greatest inactivation of Salmonella (5.68 log CFU g −1 ) and the highest PPO inactivation (78%) were observed after dipping cut apples in 5% CA, followed by 3 min of CP treatment. The color of cut apples remained relatively unchanged, with a fresh‐like appearance during 7 days of storage at 4 °C after this combined treatment. Although the low‐pressure cooling time was increased when samples were pre‐dipped in CA, related to those undipped, the moisture loss was reduced by more than 50% during the PiLPC process. No significant reduction in phenolic content was observed during the PiLPC when the samples were pre‐dipped in 5% CA. CONCLUSION These results indicate the potential of this integrated process for the inactivation of endogenous food enzymes and bacterial pathogens in fresh‐cut apples. © 2021 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) | 0.000 | 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".