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Record W4414367536 · doi:10.1002/9781119265665.ch25

Ultraviolet Light from Emerging Technology to Commercialization

2025· other· en· W4414367536 on OpenAlexaff
Tatiana Koutchma

Bibliographic record

Venuenot available
Typeother
Languageen
FieldPhysics and Astronomy
TopicGaN-based semiconductor devices and materials
Canadian institutionsAgriculture and Agri-Food Canada
Fundersnot available
KeywordsCommercializationUltraviolet lightFood spoilageEmerging technologiesFood safetyProcess (computing)Technology developmentProduct (mathematics)Food technology

Abstract

fetched live from OpenAlex

Ultraviolet (UV) light is a novel technology that quickly advances as a dry, purely physical, nonthermal, and nonchemical technology in the food chain because UV can effectively control food-, air-, and water-born microbial and viral hazards, spoilage microbiota, and leave no residues in foods, food contact surface, and equipment. UV light-based solutions have shown tremendous growth and potential for food plants and products safety applications in recent years particularly during COVID-19 pandemic. This chapter reviews the current status of commercial applications of UV light technology in food facilities and processing systems, as well as principles of process design and validation for preservation of solid and liquid foods and beverages. The essential process and systems requirements including safety of UV light are discussed and have to be considered for successful UV implementation. The knowledge gaps in product and process design that need to be addressed to accelerate light technology commercialization are also presented.

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)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: none
Teacher disagreement score0.022
Threshold uncertainty score0.073

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0040.004
Open science0.0010.001
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0220.008

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 head0.007
GPT teacher head0.266
Teacher spread0.259 · 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 designNot applicable
Domainnot available
GenreReview

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

Citations1
Published2025
Admission routes1
Has abstractyes

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Same topicGaN-based semiconductor devices and materialsFrench-language works237,207