Ohmic Heating in the Food Industry: Developments in Concepts and Applications during 2013–2020
Bibliographic record
Abstract
Various technologies have been evaluated as alternatives to conventional heating for pasteurization and sterilization of foods. Ohmic heating of food products, achieved by passage of an alternating current through food, has emerged as a potential technology with comparable performance and several advantages. Ohmic heating works faster and consumes less energy compared to conventional heating. Key characteristics of ohmic heating are homogeneity of heating, shorter heating time, low energy consumption, and improved product quality and food safety. Energy consumption of ohmic heating was measured as 4.6–5.3 times lower than traditional heating. Many food processes, including pasteurization, roasting, boiling, cooking, drying, sterilization, peeling, microbiological inhibition, and recovery of polyphenol and antioxidants have employed ohmic heating. Herein, we review the theoretical basis for ohmic treatment of food and the interaction of ohmic technology with food ingredients. Recent work in the last seven years on the effect of ohmic heating on food sensory properties, bioactive compound levels, microbial inactivation, and physico-chemical changes are summarized as a convenient reference for researchers and food scientists and engineers.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 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".