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Effect of Postharvest LED Application on Phenolic and Antioxidant Components of Blueberry Leaves

2018· article· en· W2900605551 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.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueChemEngineering · 2018
Typearticle
Languageen
FieldMedicine
TopicPhytochemicals and Antioxidant Activities
Canadian institutionsMcGill University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsAnthocyaninDPPHChemistryAntioxidantFood sciencePostharvestFlavonoidCultivarFerricBotanyPhenolsExtraction (chemistry)ChromatographyBiologyBiochemistryOrganic chemistry

Abstract

fetched live from OpenAlex

Light from red (661 nm) and blue (417 nm) LEDs were applied for 12, 24, and 48 h on freshly harvested blueberry leaves of different cultivars mixed together. The extracts obtained through microwave extraction of these leaves were analysed in terms of total phenolic content, total monomeric anthocyanin content, and antioxidant activity as measured by % scavenging 2,2-diphenyl-1-picrylhydrazyl (DPPH) scavenging activity and ferric reducing antioxidant potential (FRAP). It was observed that although the content of total phenolic content was high in the untreated leaves, there was an increase in the phenolic content and monomeric anthocyanin content of the leaves treated with blue light. DPPH inhibition activity and FRAP for all the samples were high; however, there was an increase in the FRAP of samples treated with light for different durations, which varied with type of light and the time of application of the LED light.

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)0.000
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: Empirical · Consensus signal: Empirical
Teacher disagreement score0.043
Threshold uncertainty score0.337

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 integrity0.0000.000
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 head0.006
GPT teacher head0.250
Teacher spread0.243 · 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