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Record W3012241772 · doi:10.1080/87559129.2020.1740246

Non-thermal Technology and Heating Technology for Fresh Food Cooking in the Central Kitchen Processing: A Review

2020· review· en· W3012241772 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

VenueFood Reviews International · 2020
Typereview
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicMicrobial Inactivation Methods
Canadian institutionsMcGill University
FundersNational Key Research and Development Program of China
KeywordsFood processingQuality (philosophy)Product (mathematics)Process engineeringFood technologyFood industryConvenience foodFood qualityAutomationEfficient energy useBusinessEnvironmental scienceFood scienceManufacturing engineeringEngineeringChemistryMathematicsMechanical engineering

Abstract

fetched live from OpenAlex

The central kitchen model has promoted the industrialization of the catering industry. The quality of central kitchen products is related to processing technology. The new non-thermal technology and heating technology not only have advantages over traditional technologies in improving product quality and safety, but also have more precise control over the processing process and a higher degree of automation. Using non-thermal technology conditioning before cooking can change the properties of food ingredients, and improve the quality and safety of cooked food. The new heating technology replaces the traditional heating method to provide thermal energy for food cooking, and has the advantages of shortening cooking time, improving quality attributes, improving processing efficiency and product safety. This article reviews the application and research progress of non-heating and heating technologies in fresh food processing in the central kitchen.

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)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.980
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
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 head0.054
GPT teacher head0.377
Teacher spread0.323 · 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