Emerging Preservation Methods for Fruit Juices and Beverages
Bibliographic record
Abstract
Food Additive 66 Traditional thermal pasteurizationThermal processing is the most widely used technology for pasteurization of fruit juices and beverages.Juice pasteurization is based on a 5-log reduction of the most resistant microorganisms of public health significance (USFDA 2001).The process could be accomplished by different time-temperature combinations. Low temperature long time (LTLT)Fruit juice has been traditionally pasteurized by batch heating at 63-65°C for relatively long time (D'Amico et al. 2006).This method has been replaced by high temperature short time treatment due to the undesirable quality changes during this process. High temperature short time (HTST)HTST treatment could minimise those undesirable quality changes made by batch heating due to the much less duration of heat treatment.Currently, HTST pasteurization is the most commonly used method for heat treatment of fruit juice.For example, orange juice is processed by HTST at 90 to 95°C for 15 to 30 s (Braddock 1999).And apple juice is treated by HTST at 77 to 88°C for 25 to 30 s (Moyer & Aitken 1980). How to referenceIn order to correctly reference this scholarly work, feel free to copy and paste the following:
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 0.005 |
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".