Comparative effects of dielectric barrier discharge atmospheric cold plasma and thermal pasteurization on mandarin juice safety and quality during storage
Bibliographic record
Abstract
This study systematically evaluated the effects of thermal pasteurization (TP) and dielectric barrier discharge atmospheric cold plasma (DBD ACP) on microbial inactivation, nutritional composition, enzymatic activity, and sensory attributes of fresh mandarin juice during 18 days of storage at 4 °C. TP was applied at 95 °C for 60 s, while DBD ACP treatments were conducted for durations ranging from 30 to 120 s. TP effectively inactivated spoilage microorganisms and quality-degrading enzymes, reducing microbial counts to acceptable levels and enzymatic activities (polyphenol oxidase and pectin methylesterase) to below 20%. However, TP significantly compromised nutritional quality, with notable reductions in ascorbic acid (26%), total phenolics, and antioxidant capacity. Additionally, TP negatively impacted sensory properties, inducing heat-related pigment degradation and the development of off-flavors. In contrast, DBD ACP treatments, particularly at 30–60 s, preserved ascorbic acid, phenolic content, and antioxidant activity, maintaining values statistically similar to those of the untreated juice samples. The 120-s DBD ACP treatment achieved microbial reduction comparable to TP (total plate count: 3.30 log CFU/mL; yeast and molds: 2.40 log CFU/mL), but demonstrated limited efficacy in enzyme inactivation, with residual enzyme activities exceeding 80%. Sensory evaluation indicated that DBD ACP-preserved juice retained its color, aroma, flavor, and taste, with scores closely aligned with those of the control sample. Results suggest that while DBD ACP is effective in preserving nutritional and sensory quality, its limited ability to inactivate enzymes may necessitate a combination with other preservation methods. Overall, DBD ACP represents a promising non-thermal processing technology for the safety and preservation of fresh fruit juices.
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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 | 0.001 |
| 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 | 0.001 | 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".