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The effect of high pressure treatment on rheological characteristics and colour of mango pulp

2005· article· en· W1975540938 on OpenAlexaff
Hosahalli S. Ramaswamy, NIKHIL D. HIREMATH

Bibliographic record

VenueInternational Journal of Food Science & Technology · 2005
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicMicrobial Inactivation Methods
Canadian institutionsMcGill University
Fundersnot available
KeywordsPulp (tooth)RheologyConsistency indexHigh pressurePulp and paper industryChemistryFood scienceComposite materialMaterials scienceDentistryMedicine

Abstract

fetched live from OpenAlex

Abstract The effect of high-pressure (HP) treatment (100–400 MPa for 15 or 30 min at 20 °C) on the rheological characteristics and colour of fresh and canned mango pulps was evaluated. Differences were observed in the rheological behaviour of fresh and canned mango pulps treated with HP. Shear stress–shear rate data of pulps were well described by the Herschel–Bulkley model. The consistency index (K) of fresh pulp increased with pressure level from 100 to 200 MPa while a steady decrease was noticed for canned pulp. For fresh pulp the flow behaviour index decreased with pressure treatment whereas an increasing trend was observed with canned pulp. Storage and loss moduli of treated fresh pulp with HP increased linearly with angular frequency up to 200 MPa for a treatment time of 30 min while a steady decreasing trend was found for processed pulp. No significant variation in colour was observed during pressure treatment.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.002
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

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 integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.009
GPT teacher head0.303
Teacher spread0.294 · 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 source (direct Gemma or distilled Codex), 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

Citations165
Published2005
Admission routes1
Has abstractyes

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