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Record W2052679735 · doi:10.1080/10942910802105460

Dielectric Properties of Potato Puree in Microwave Frequency Range as Influenced by Concentration and Temperature

2009· article· en· W2052679735 on OpenAlexaff
Jasim Ahmed, Nadide Seyhun, Hosahalli S. Ramaswamy, Giorgio Luciano

Bibliographic record

VenueInternational Journal of Food Properties · 2009
Typearticle
Languageen
FieldEngineering
TopicMicrowave and Dielectric Measurement Techniques
Canadian institutionsMcGill UniversityPolymer Source (Canada)
Fundersnot available
KeywordsDielectricSlurryMaterials scienceAnalytical Chemistry (journal)MicrowavePotato starchStarch gelatinizationAtmospheric temperature rangePenetration depthPenetration (warfare)ChemistryStarchComposite materialChromatographyFood scienceThermodynamicsOptics

Abstract

fetched live from OpenAlex

Dielectric properties of potato flour-water dispersions (slurry) were measured in the frequency range of 500–2500 MHz by the open-ended coaxial probe method using a network analyzer as a function of concentration (10–25% w/w) and temperature (20–75oC). Both commercial and laboratory prepared samples of potato flours were used. Results indicated that the dielectric constant (ε′) decreased with temperature and frequency while increased with concentration. The loss factor (ε″) increased with frequency and concentration; however, temperature showed mixed effect. Both ε′ and ε″ data in conventionally used microwave frequencies (915 and 2450 MHz) were studied as function of concentration and temperature for two sample types using a response surface methodology and found to follow 2nd order polynomial models. Temperature and concentration contributed significantly on dielectric spectra of potato slurry and the sample source had some effect. A change in ε′ and ε″ above 70oC could be attributed by starch gelatinization. Penetration depth (Dp) decreased with an increase in frequency and non-systematic with temperature. Addition of salt substantially reduced Dp of potato slurry.

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 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)0.000
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 score0.027
Threshold uncertainty score0.412

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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 head0.012
GPT teacher head0.208
Teacher spread0.196 · 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 teacher head, 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

Citations14
Published2009
Admission routes1
Has abstractyes

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