Bibliographic record
Abstract
This paper brings to perspective issues related to research initiatives for the application of microwave (MW) and radiofrequency (RF) applications in foods. Both MW (300 MHz and 300 GHz) and RF waves (3 kHz — 300 MHz) are part of the electromagnetic spectrum that result in heating of dielectric materials by induced molecular vibration as a result of dipole rotation or ionic polarization. They have been credited with volumetric heat generation resulting in rapid heating of foodstuffs. Due to their lower frequency levels, RF waves have a larger penetration depth than MW and hence could find better application in larger size foods. Besides the popular domestic use of MW ovens, commercialized applications of MW/RF heating include blanching, tempering, pasteurization, sterilization, drying, rapid extraction, enhanced reaction kinetics, selective heating, disinfestations, etc. This paper reviews the current status and research needs for in-packaged sterilization technologies for commercial applications. Technological challenges include process equipment design, microbial destruction and enzyme inactivation kinetics, temperature and process monitoring, and achieving of temperature uniformity. Other issues also relate to the use of packaging material in in-package sterilization applications, package/container concerns in domestic MW ovens, receptor technology for creating dry-oven conditions, modeling and time-temperature process integrators. There is also the issue of non-thermal and enhanced thermal effects of microwave heating on destruction kinetics.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".