Electrical Conductivity of Cabbage and Daikon Radish as Affected by Electrical Voltage, Frequency, Salt Concentration and Temperature
Bibliographic record
Abstract
Abstract Shredded cabbage (50% v/v) and Daikon radish cubes (57% v/v) were mixed with different salt solutions (0.15, 0.5, 1, 1.5 and 1.85%), poured into a Teflon‐coated static Ohmic cell and heated from 30 to 70°C at different alternating current voltages (65, 80, 100, 120 or 135 V) and frequencies (60, 2,070, 5,030, 7,990 or 10,000 Hz). Voltage, current, time and temperature were measured to calculate electrical conductivities at different temperatures. For the modeling part, 750 g of a blended crude puree (particle size < 0.5 mm) was used to fill the Ohmic cell. Daikon radish gave the highest value for electrical conductivity of 1.07 S/m at 30°C and 1.85%, 100 V and 5,030 Hz while cabbage gave a value of 0.81 S/m under the same conditions. For cabbage, the electrical conductivity values increased with increasing frequency at higher voltage, but decreased at low voltage levels. An opposite trend was observed for Daikon radish. Modeling indicated that electrical conductivity increased quadratically with temperature, salt concentration and electrical voltage. Response surface models revealed that linear, cross products, as well as quadratic effects were significant with R2 > 0.98. The Maxwell–Eucken model, which describes solid particles dispersed in continuous liquid, showed good fit for the electrical conductivity data. Practical Applications Ohmic heating can be an alternative to conventional heat processing of food, reducing treatment time and improving quality. Ohmic heating has been generally recognized to provide uniform and rapid heating conditions. The electrical conductivity (EC) of food components is the key property in the Ohmic heating process and is dependent on many product and system‐dependent properties, especially the salt content. It is also the primary property needed for modeling the Ohmic heating effects of a system or product. This manuscript details the gathered data on electrical conductivity of cabbage and radish, and an appropriate modeling approach to describe their dependence on product and system properties. The study generates new data EC for the vegetables and how the EC is influenced by the two vegetables that differ significantly in structure.
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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".