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Record W4414367907 · doi:10.1002/9781119265665.ch9

Biochemical Aspects of High‐Pressure Food Processing

2025· other· en· W4414367907 on OpenAlexaff
Fatemah B. Alsalman, Hosahalli S. Ramaswamy

Bibliographic record

Venuenot available
Typeother
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicMicrobial Inactivation Methods
Canadian institutionsMcGill University
Fundersnot available
KeywordsPascalizationFood industryFood processingFood productsFood qualityFood technology

Abstract

fetched live from OpenAlex

High pressure processing (HPP) nowadays is used as an alternative to thermal treatment. HPP that uses in food industry has many applications, and the parameters of the HPP system depend on the application used and products’ composition. HPP can improve many properties including structural and functional in addition to digestibility and safety without or with minimal effect on its flavor, texture, or color. Sometimes HPP is combined with thermal treatment for better results, especially for microbial inactivation. HPP is a promising technology where there are many opportunities for food industries to develop new food products since it affects biomolecules such as carbohydrates, lipids, proteins, and nucleic acids, especially aiding in modifying their functional properties. HPP can meet consumers’ preferences with the best food quality when all the parameters are optimized such as the temperature, pressurization holding time, compression and decompression rate, and pressure magnitude.

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: Other · Consensus signal: Other
Teacher disagreement score0.062
Threshold uncertainty score0.974

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 integrity0.0010.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.010
GPT teacher head0.284
Teacher spread0.274 · 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
GenreOther

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
Published2025
Admission routes1
Has abstractyes

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