Feasibility of a Novel Industrial-Scale Treatment of Green Cold-Pressed Juices by UV-C Light Exposure
Bibliographic record
Abstract
A novel industrial-scale ultraviolet-C (UV-C) light processor from AseptoRay (MGT, Israel) was used to treat a raw cold-pressed green juice blend (GJB) consisting of kale, romaine, celery, apple, and lemon. The effect of UV-C light energies of 0.88 kJ L−1 and 2.93 kJ L−1 on microbial, enzymatic, nutritional, quality, and sensory parameters of the GJB was studied. Using 2.93 kJ L−1, 3.7 log reduction in aciduric bacteria and 3.9 logs in aerobic colony count were achieved, while lactic acid bacteria, coliforms, yeasts, and moulds were reduced by >3, >2, 2.1, and 2.1 logs, respectively. A minor increase in polyphenoloxidase (PPO) enzyme activity was seen with 0.88 kJ L−1 and a slight change in colour (not visually observed) was detected using 2.93 kJ L−1. No other significant change in nutritional and quality parameters or enzyme activities was detected. Further, the stability of the GJB was explored. Kale and romaine contributed the most significant source of spoilage enzyme activity, cloud loss, and browning in the GJB. These stability parameters were shown to be affected by pressing temperature and pH. The commercial UV-C treatment process explored in this study is a viable alternative to high pressure processing (HPP) for improved microbial safety of fresh green juice blends.
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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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".