Effects of Ultraviolet Light and High‐Pressure Processing on Quality and Health‐Related Constituents of Fresh Juice Products
Bibliographic record
Abstract
Fresh juices are highly popular beverages in the global food market. They are perceived as wholesome, nutritious, all-day beverages. For a fast growing category of premium juice products such as cold-pressed juices, minimal-processing nonthermal techniques such as ultraviolet (UV) light and high-pressure processing (HPP) are expected to be used to extend shelf-life while retaining physicochemical, nutritional, and sensory characteristics with reduced microbial loads. Also, UV light and HPP are approved by regulatory agencies and recognized as one of the simplest and very environmentally friendly ways to destroy pathogenic organisms. One of the limitations to their more extensive commercial application lies in the lack of comparative effects on nutritional and quality-related compounds in juice products. This review provides a comparative analysis using 92 studies (UV light: 42, HPP: 50) mostly published between 2004 and 2015 to evaluate the effects of reported UV light and HPP processing conditions on the residual content or activity of bioactive compounds such as vitamins, polyphenols, antioxidants, and oxidative enzymes in 45 different fresh fruit and vegetable juices (low-acid, acid, and high-acid categories). Also, the effects of UV light and HPP on color and sensory characteristics of juices are summarized and discussed.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".