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Record W4310668868 · doi:10.1111/1750-3841.16408

Microwave heating process of moderate‐minced surimi based on multiphase porous media model

2022· article· en· W4310668868 on OpenAlexaff
Bowen Yan, Linglu Meng, Huayu Yang, Lin Du, Xidong Jiao, Nana Zhang, Jianlian Huang, Jianxin Zhao, Hao Zhang, Wei Chen, Daming Fan

Bibliographic record

VenueJournal of Food Science · 2022
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicFood Drying and Modeling
Canadian institutionsMinistry of Agriculture
FundersJiangsu Agricultural Science and Technology Innovation Fund
KeywordsPorosityMicrowaveMaterials scienceRefractive indexPorous mediumMicrowave heatingMass transferDielectricComposite materialChemical engineeringThermodynamicsAnalytical Chemistry (journal)ChromatographyChemistry

Abstract

fetched live from OpenAlex

Moderately processed surimi products exhibit better nutrient retention and enhanced gels, and the great potential of microwaves application and changes in the way of chopping meat has been reported by previous research. In this study, a systematic analysis of the novel surimi product was made to explore the heat and mass transfer characteristics. A porous media model combining electromagnetic heat and hygroscopic expansion was developed to evaluate this process, and its accuracy has been verified experimentally. It was found that the dielectric characterization of multiphase mixture system has great influence on the results, the complex refractive index mixture equation was used due to its lowest root-mean-square error value. In addition, the effect of moderate processing on microwave heating was examined in terms of porosity changes. However, nonuniform temperature distributions were found in the higher porous samples, especially when the porosity is greater than 0.81. Moreover, the developed model was coupled with the evaluation for gel properties and the results showed the significant effect of moderate crushing on the gel quality during the microwave heating process.

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)0.001
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: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.313
Threshold uncertainty score0.399

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.048
GPT teacher head0.264
Teacher spread0.216 · 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 designSimulation or modeling
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
Published2022
Admission routes1
Has abstractyes

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