Release of Electrode Materials and Changes in Organoleptic Profiles During the Processing of Liquid Foods Using Pulse Electric Field Treatment
Bibliographic record
Abstract
Application of pulsed electric fields (PEFs) is an alternative treatment to thermal pasteurization of liquid foods, which inactivates microorganisms without degrading flavor, texture, and nutrients compared to other conventional technologies. This article scrutinizes the applicability of PEF processing of carbonated beer using a titanium electrode based sealed processing chamber that provides not only effective use of the highest electric field but also eliminates edge effects, and at the same time reduces turbulence by providing steady and uniform flow of the fluid under treatment. The release of metal ions during PEF processing is evaluated by using inductively coupled plasma atomic emission spectrometry (ICP-AES). As metal ions directly affect the organoleptic properties of beer, a sensory evaluation is conducted with the panelists comprised of brewing experts. It has been found that the released amounts of metal ions are lower than the detection levels of ICP-AES, and much lower than what is accepted in consumable foods (<; 2 parts per billion). Supporting the metal analysis data, the sensory panel also reported nonoff flavors in the PEF-processed beer. The treated and untreated beer samples are aged at 5 °C, 22 °C, and 34 °C for ten days. The shelf-life analysis is conducted for PEF-treated and untreated beer samples. The organoleptic property profile of the beer treated at 4 kV by PEF shows the potential of extending shelf life at least for 90 days. In addition, analytical indicators like the deterioration of trans-iso-α-acids confirm no chemical changes in beer before and after PEF treatment under the conditions studied.
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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 | 0.000 | 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".