Pulsed electric field assisted juice extraction
Bibliographic record
Abstract
Purpose of the Review: Recently, there has been ren ew ed interest in the extraction of juice and functional ingredients from fruits and v egetables. Several consumer-related factors support the adoption of non -thermal technologies. This paper reviews application of pu lsed electric field (PEF) in juice extraction processes. It highlights novel designs of extractors that allow simultaneous applicati on of PEF and p ressure. Parameters that influence juice extracti on and juice quality issues are also discussed. Ma in findings: PEF can be used successfully to induce electroplasmolysis or ho mogenisation of tissues and intensify juice extraction fro m fruits and vegetable materials. The PEF must be applied strategically in order to optimise juice extraction. D irections for Futu re Research: Future studies on PEF-assisted juice extraction will involve applications to extract specific chemical c omponents alone with the juice, optimisation of the different PEF p arameters to obtain desired extraction kinetics and detailed ch emical evaluation of extracts to validate quality. There is currently limited or no commercially available equipment. Equipment manuf actur ers may be involved to design and fabricate easy-to-use PEF extractors. Ke ywords: Pu lsed electric field; juice extraction; elect roplasmolysis; juice quality; pressure; energy
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".