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Record W2183892406 · doi:10.82308/42378

Thermal and rheological properties of batter systems

2007· dissertation· en· W2183892406 on OpenAlexfundno aff

Bibliographic record

VenueeScholarship@McGill (McGill) · 2007
Typedissertation
Languageen
FieldAgricultural and Biological Sciences
TopicPolysaccharides Composition and Applications
Canadian institutionsnot available
FundersFonds Québécois de la Recherche sur la Nature et les TechnologiesNatural Sciences and Engineering Research Council of CanadaMcGill University
KeywordsRheologyComputer scienceMaterials scienceComposite material

Abstract

fetched live from OpenAlex

Batters are highly complex systems with wide ranging ingredients including flours, water, flavorings, and spices. Interactions between the ingredients determine the performance of batters and the final quality of coated products. Addition of hydrocolloids into batters of different types of flour provides special effects on batter performance. The functionalities of hydrocolloids-flour mixtures in terms of the thermal and rheological properties of the resulting batter systems were investigated in this study. The rheological properties of the batter were determined using a strain/stress control rheometer. A steady state method was used to measure the viscosity as a function of the shear rate varying from 0.5 to 150 s-1 at 15°C. The resulting data was then fitted to the Herschel-Bulkley Model. The viscoelastic properties were monitored as a function of temperature and were determined using a dynamic oscillatory test. Two different temperature profiles were used to simulate cooking and storage processes. Differential scanning calorimetry (DSC) was used to determine thermal properties (namely glass transition temperature, gelatinization temperature, ice melting temperature, and enthalpy) and to describe the phase transitions that occur during heating and cooling processes. The rheological and thermal properties varied for different types of flours and their combination ratios, as well as different types of hydrocolloids at different concentrations. The replacement of corn flour greatly altered the viscosity and viscoelastic properties of wheat based and rice based batter systems. Using 100% corn flour based batter showed highest yield stress, whereas 100% rice flour based batter did not show any yield stress. Higher temperatures and longer times were required to gelatinize starch at the higher levels of rice flour for each batter system flour mix combination. The various combination ratios of the flours apparently did not significant influence the gelatinization temperatures of the batter systems. However they significantly influence the total enthalpies (DeltaHG) of the various samples. Wheat flour based batters showed the lowest glass transition temperatures. Thermal properties of wheat-based batters were influenced by the replacement of wheat with rice or corn flours. Corn flour based batters required considerably more energy for gelatinization during the cooking process. Hydrocolloids lowered flow behavior index (n) and increased the consistency index (k) of all batters. The gums also changed the onset temperature of structure development and the storage and loss moduli of the batter systems. Hydrocolloids greatly influenced the thermal properties of batter systems. The gums shifted gelatinization temperature and depressed glass transition temperature of resulting batter systems. Further, MC increased the melting temperature (Tm) for the test batter systems as compared with the values for the control system without methylcellulose (MC). Carboxymethylcellulose (CMC) did not show statistically significant effects on the total enthalpies of ice melting for all samples. However, MC and CMC showed more pronounced effects on rice, corn, and their combined flour based batters than it did on wheat flour based batters. However, this characteristic does not show in batter systems containing xanthan gum.

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 score0.055
Threshold uncertainty score0.613

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 studies0.0010.000
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 head0.028
GPT teacher head0.232
Teacher spread0.205 · 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

Citations0
Published2007
Admission routes1
Has abstractyes

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