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Record W2743623420 · doi:10.1094/cchem-11-16-0265-r

Frozen Convenience Noodles: Use of Ultrasound to Study the Influence of Preparation Methods on Their Rheological Parameters

2017· article· en· W2743623420 on OpenAlexafffund
Daiva Daugelaite, Anatoliy Strybulevych, Tomohisa Norisuye, David W. Hatcher, Martin G. Scanlon, J. H. Page

Bibliographic record

VenueCereal Chemistry · 2017
Typearticle
Languageen
FieldNursing
TopicFood composition and properties
Canadian institutionsUniversity of Manitoba
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsChemistryBlanchingFood scienceRheologyChewinessRaw materialComposite materialMaterials science

Abstract

fetched live from OpenAlex

Longitudinal ultrasonic waves were used to investigate effects of preparation and frozen storage time on the rheological properties of noodles. Noodles prepared with and without glucose oxidase (GOx) were flash frozen either immediately after production (raw), after blanching, or after being optimally cooked. From measurements of attenuation, phase velocity, loss and storage moduli ( M″ and M′ ), and tan δ L ( M″ / M′ ), it was found that raw noodles, prepared with or without GOx, were most similar overall to fresh noodles when stored frozen for one week. However, blanched noodles were closest to fresh noodles in terms of firmness, as measured by storage modulus, after one week of storage. Frozen storage for four weeks resulted in a significant loss in noodle quality. Stress relaxation measurements of Peleg's K 1 and K 2 parameters showed a significant effect of GOx addition on raw noodles after one week of frozen storage. As shown by K 1 and K 2 parameters, GOx addition delayed noodle texture deterioration associated with frozen aging for blanched and cooked treatments. The combined texture assessment of stress relaxation coupled with simultaneous ultrasonic measurements exhibits good potential for examining how formulation and pretreatment can mitigate the textural quality impairments associated with freezing of convenience noodles.

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)0.001
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 score0.064
Threshold uncertainty score0.271

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 head0.088
GPT teacher head0.365
Teacher spread0.277 · 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

Citations12
Published2017
Admission routes2
Has abstractyes

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