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Record W2909174012 · doi:10.3390/foods8010039

Effect of Soluble Solids and High Pressure Treatment on Rheological Properties of Protein Enriched Mango Puree

2019· article· en· W2909174012 on OpenAlexaff
Hosahalli S. Ramaswamy, Anuradha Gundurao

Bibliographic record

VenueFoods · 2019
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicProteins in Food Systems
Canadian institutionsMcGill University
Fundersnot available
KeywordsRheologyFood scienceChemistryBrixViscosityWhey proteinApparent viscosityHigh pressureTotal dissolved solidsMaterials scienceComposite materialSugarThermodynamics

Abstract

fetched live from OpenAlex

The effects of high pressure treatment on the rheological properties of protein enriched mango puree were evaluated. In the first part, the original soluble solids in mango puree (28° Brix) was lowered to 26, 23 and 20° Brix, and each was supplemented with 2, 5 and 8% of whey protein to assess the influence of added protein. The samples were then evaluated by dynamic rheology. Pressure treatment resulted in a progressive increase in elastic (G') and viscous modulii (G″) as well as complex viscosity (η*). Values of G' were higher than G″ demonstrating that the product behaved more like a gel. Additional tests were carried out by simultaneously varying protein and soluble solids contents, and keeping the total solids at 28%. In this case, the effects of pressure levels and holding times were similar to previous results. However, the positive effects of higher protein were negated by the negative effects of lower soluble solids, resulting in an overall decreasing effect on rheology. The developed models effectively predicted the combined influence of protein and soluble solid concentrations on rheological parameters (R2 > 0.85). Sensory evaluation of 2% and 5% protein supplemented and pressure treated (500 MPa/3 min) mango puree yielded acceptable sensory qualities, resulting in a product with enriched protein content.

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 score0.028
Threshold uncertainty score0.185

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 head0.020
GPT teacher head0.219
Teacher spread0.199 · 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

Citations9
Published2019
Admission routes1
Has abstractyes

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