Bibliographic record
Abstract
The microbiological shelf life of carbonated beer can be extended after being processed by pulsed electric fields (PEFs). Being a nonthermal preservation method, the freshlike taste and the original nutritional value are better retained than with conventional thermal pasteurization (TP). Brewery regulations in Canada do not necessitate the TP of carbonated beer; hence, PEF processing finds a potential application in the beer industry. This paper investigates the applicability of the PEF processing to carbonated beer, particularly through a sealed and pressurized processing chamber. However, due to the inevitable flow of current during the PEF processing, metal ions will be released from the electrodes. The presence of these ions in PEF-processed beer accelerates its aging and may affect the organoleptic stability. Hence, we further evaluate the metal ions released during the PEF processing and their consequent effect, if any, on the taste and the shelf life. It has been found that the released amounts of iron ions are observed to be within the acceptable levels for consumable liquid foods. Untrained participants from the public preferred the PEF-processed beer when compared with thermally pasteurized beer. Trained panelists reported a metallic feeling in the PEF-processed beer corresponding to the presence of metal ions that was detected analytically.
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) | 0.000 | 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".