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Record W2259818086 · doi:10.1111/jfpe.12315

Electrical Conductivity of Cabbage and Daikon Radish as Affected by Electrical Voltage, Frequency, Salt Concentration and Temperature

2015· article· en· W2259818086 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueJournal of Food Process Engineering · 2015
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicMicrobial Inactivation Methods
Canadian institutionsAgriculture and Agri-Food CanadaMcGill University
Fundersnot available
KeywordsElectrical resistivity and conductivityOhmic contactConductivityVoltageChemistryAnalytical Chemistry (journal)Materials scienceElectrical engineeringElectrodeChromatography

Abstract

fetched live from OpenAlex

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 R 2 > 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.

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.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.010
Threshold uncertainty score0.405

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.009
GPT teacher head0.263
Teacher spread0.254 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it