Energy Requirements for Alternative Food Processing Technologies—Principles, Assumptions, and Evaluation of Efficiency
Bibliographic record
Abstract
Abstract Alternative food preservation technologies include substitutes to heating methods that may have benefits that include reduction of energy consumption. High‐pressure processing (HPP), membrane filtration (MF), pulsed electric fields (PEF), and ultraviolet radiation (UV) are examples of alternative preservation technologies of growing commercial interest. As unit operations these technologies operate in 4 modes of energy transfer: momentum, heat, electromagnetic, or photon transfer. The objectives of this review were: (1) to examine the fundamentals of energy requirements of 4 alternative food processing technologies such as HPP, MF, PEF, UV, and conventional high‐temperature short‐time (HTST) processing, (2) to establish a basis for comparison of energy consumption between or within technologies, and (3) to evaluate specific energy requirements for the 5 technologies to achieve required safety performance in apple juice. Three levels of energy evaluation for each technology including internal energy, applied energy, and consumed energy were reviewed. The comparison of the specific energy for the 5 technologies was based on information published in scientific papers where the inactivation of Escherichia coli in apple juice was explored. Based on the analysis of energy consumption of these technologies it was concluded that MF and UV have the potential to consume less specific energy than HTST, PEF, and HPP. Differences in energy consumption within each group of technologies were also observed and these could be attributed to differences in the systems. The differences in energy consumption within each group of technologies illustrate that there is potential of improvement in most technologies.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".