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Record W2582735137 · doi:10.21273/hortsci.41.4.988c

Effects of Postharvest Storage and UV-C Irradiation on the Phenolic Content and Antioxidant Capacity of Cranberries

2006· article· en· W2582735137 on OpenAlexaff
Wilhelmina Kalt, Agnes M. Rimando, Michele Elliot, Charles F. Forney

Bibliographic record

VenueHortScience · 2006
Typearticle
Languageen
FieldMedicine
TopicPhytochemicals and Antioxidant Activities
Canadian institutionsNova Scotia Department of AgricultureAgriculture and Agri-Food Canada
Fundersnot available
KeywordsPostharvestChemistryFood scienceAnthocyaninAntioxidantAntioxidant capacityResveratrolIrradiationFood preservationBotanyBiologyBiochemistry

Abstract

fetched live from OpenAlex

Recent interest in the human health-promoting properties of fruit phenolics, and especially fruit flavonoids, has stimulated research on how these secondary metabolites may be affected by pre- and postharvest horticultural factors. Resveratrol, although a minor phenolic in many fruit, possesses potent bioactivities, and is therefore of particular interest. To study the effects of postharvest storage and UV-C irradiation on selected phenolic components and antioxidant capacity of cranberry ( Vaccinium macrocarpon ), fruit of cv. Pilgrim, Stevens, and Bergman, were irradiated with UV-C at levels between 0 and 2.0 KJ·m -2 , followed by storage at 9 °C for 7 and 17 d. Total phenolic content did not change during storage. However, resveratrol content was higher and antioxidant capacity (ORAC) was lower at 7 days of storage compared to 17 days. There was no main effect of UV-C on total phenolics, anthocyanins, resveratrol, or ORAC. However, there was an interaction between storage time and UV-C irradiation. Anthocyanin content was lower at 7 days, and higher at 17 days, at UV dosages of 1.0 or 2.0 KJ·m -2 . Resveratrol content was higher in UV-C irradiated fruit at 7 days, while at 17 days there was no difference between UV-treated and untreated fruit.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.588
Threshold uncertainty score0.214

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 studies0.0000.001
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 head0.018
GPT teacher head0.214
Teacher spread0.196 · 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
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

Citations0
Published2006
Admission routes1
Has abstractyes

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