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Pasteurization of Fresh Orange Juice Using Low‐Energy Pulsed Electrical Field

2002· article· en· W2009382722 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 Science · 2002
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicMicrobial Inactivation Methods
Canadian institutionsUniversity of Guelph
Fundersnot available
KeywordsPasteurizationChemistryOrange juiceFood scienceNisinAromaOrange (colour)Shelf lifePopulationSterilization (economics)Vitamin CChromatographyAntimicrobial

Abstract

fetched live from OpenAlex

ABSTRACT: Using the hurdle approach, temperature, acidity, and number of pulses were varied to maximize microbial inactivation in orange juice. The effect of PEF combined with the addition of nisin, lysozyme, or a combination of both to orange juice was also investigated. Optimal conditions consisting of 20 pulses of an electric field of 80 kV/ cm, at pH 3.5, and a temperature of 44 °C with 100 U nisin/ml resulted in over a 6‐log cycle reduction in the microbial population. The process was most influenced by a change in temperature (p < 0.0001). Following treatment, there was a 97.5% retention of vitamin C, along with a 92.7% reduction in pectinmethylesterase activity. The microbial shelf‐life of the orange juice was also improved and determined to be at least 28 d when stored at 4 °C without aseptic packaging. Gas chromatography revealed no significant differences in aroma compounds before and after pulsing.

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)0.001
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 score0.079
Threshold uncertainty score0.197

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
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 head0.031
GPT teacher head0.295
Teacher spread0.264 · 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