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HIGH PRESSURE INACTIVATION OF PECTIN METHYL ESTERASE IN ORANGE JUICE USING COMBINATION TREATMENTS

2001· article· en· W2066145180 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueJournal of Food Biochemistry · 2001
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicMicrobial Inactivation Methods
Canadian institutionsMcGill University
Fundersnot available
KeywordsChemistryOrange juicePectinPectinesteraseEsteraseBrixOrange (colour)Food scienceMethyl orangeEnzymeChromatographyBiochemistryPectinaseSugar

Abstract

fetched live from OpenAlex

The contribution of several high pressure (HP) processing related factors (pressure level, 300-400 MPa; pressure cycle, 1-3, and pressure-hold time, 30–120 min) on the inactivation of pectin methyl esterase (PME) in single strength (pH 3.7 and 11.4 °Brix) and concentrated (pH 3.5 and 42 °Brix) orange juice was evaluated. A response surface methodology was employed to model the combined effects of factors on the enzyme inactivation. The main effects were described by linear or quadratic functions. For both single strength and concentrated orange juices, the effects of all three main factors and some interactions (pressure level, cycle and holding time) were statistically significant (p<0.05). The dual nature of pressure inactivation of PME (with an instantaneous inactivation due to a pressure pulse, instantaneous pressure fall, and first order rate of inactivation during the pressure hold, yielding D and z values) reported in earlier studies was confirmed. Combination models were developed to predict the residual enzyme activity as influenced by the pressure level, number of pressure cycles and pressure hold time.

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.

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 score0.005
Threshold uncertainty score0.527

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 head0.024
GPT teacher head0.312
Teacher spread0.288 · 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