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Record W1988728234 · doi:10.1081/jfp-120016628

Determination of Electrical Parameters of Liquid Foods for Predicting Waveforms of High‐Voltage Low‐Energy Pulses

2003· article· en· W1988728234 on OpenAlexaff
Sylvia Ho, Gaurav Mittal, J.D. Cross

Bibliographic record

VenueInternational Journal of Food Properties · 2003
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicMicrobial Inactivation Methods
Canadian institutionsUniversity of WaterlooUniversity of Guelph
Fundersnot available
KeywordsWaveformVoltageOrange juiceElectrical resistivity and conductivityExponential functionElectrical networkMaterials scienceAnalytical Chemistry (journal)Biological systemChemistryMathematicsChromatographyElectrical engineeringEngineeringFood science

Abstract

fetched live from OpenAlex

The voltage waveform for pulsed electrical field across a batch treatment chamber was modeled as a function of voltage applied, time, and circuit elements. The shaping function was modeled by exponential decay and linear models. The model parameters were obtained from solutions of various electrical conductivity. The model parameters were determined for orange juice, apple cider, whole milk and skim milk and then waveforms were predicted and compared with experimental results for validation. Experimental data gave an acceptable fit to the model (r 2 = 0.853 to 0.948). The pulse waveform can be predicted for any food by knowing electrical parameters of the foods to be processed under pulsed electrical field.

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 imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.001
Threshold uncertainty score0.002

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.002
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 integrity0.0010.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.023
GPT teacher head0.277
Teacher spread0.253 · 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

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

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".

Quick stats

Citations4
Published2003
Admission routes1
Has abstractyes

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