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Record W2093774552 · doi:10.1021/jf020600c

Warm Water Treatment in Combination with Modified Atmosphere Packaging Reduces Undesirable Effects of Irradiation on the Quality of Fresh-Cut Iceberg Lettuce

2003· article· en· W2093774552 on OpenAlexaff
Xuetong Fan, P.M.A. Toivonen, Kathleen T. Rajkowski, Kimberly Sokorai

Bibliographic record

VenueJournal of Agricultural and Food Chemistry · 2003
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicPostharvest Quality and Shelf Life Management
Canadian institutionsAgriculture and Agri-Food Canada
Fundersnot available
KeywordsBrowningModified atmosphereIrradiationChemistryFood scienceVitamin CAntioxidantShelf lifeHorticultureBiologyBiochemistry

Abstract

fetched live from OpenAlex

Fresh-cut iceberg lettuce dipped in either 5 or 47 degrees C water for 2 min was packaged in modified atmosphere film bags and then exposed to 0, 0.5, 1, or 2 kGy gamma-radiation. Dipping cut lettuce in 47 degrees C water for 2 min prior to irradiation reduced antioxidant and phenolic accumulations induced by irradiation. Irradiation at 2 kGy increased cellular leakage and sogginess of cut lettuce dipped in both temperatures. Samples irradiated at 0.5 and 1 kGy had similar firmness and vitamin C and antioxidant contents as the controls after 14 and 21 days of storage except 1 kGy samples dipped at 47 degrees C had lower antioxidant contents than controls at 14 days of storage. Lettuce dipped at 47 degrees C and irradiated at 0.5 and 1 kGy had better overall visual quality and less tissue browning than corresponding irradiated samples dipped at 5 degrees C. These results suggest lettuce treated with warm water and irradiated at 0.5 or 1 kGy had the best sensory quality without significant loss in texture, vitamin C, or total antioxidants.

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)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.001
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
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)0.0010.000

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 head0.026
GPT teacher head0.223
Teacher spread0.197 · 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 designBench or experimental
Domainnot available
GenreEmpirical

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

Citations136
Published2003
Admission routes1
Has abstractyes

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