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Record W4317491746 · doi:10.1016/j.afres.2023.100273

Effects of pulsed light on the postharvest quality and shelf-life of highbush blueberries (cv. Draper)

2023· article· en· W4317491746 on OpenAlexafffund
Anubhav Pratap‐Singh, Maryam Shojaei, Anika Singh, Yutong Ye, Ronit Mandal, Yifan Yan, Joana Pico, Eric M. Gerbrandt, Simone D. Castellarin

Bibliographic record

VenueApplied Food Research · 2023
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicBerry genetics and cultivation research
Canadian institutionsBritish Columbia Blueberry CouncilBritish Columbia Institute of TechnologyUniversity of British Columbia
FundersCanada Foundation for InnovationNatural Sciences and Engineering Research Council of CanadaMitacsGovernment of Canada
KeywordsShelf lifeHorticultureEnvironmental scienceBiologyFood science

Abstract

fetched live from OpenAlex

Blueberry consumption has been a burgeoning interest attributed to their nutritional and health benefits. However, the main limitation to blueberry marketability is their perishability due to water loss, fungal, and mechanical damage during post-harvest preservation. In this study, pulsed light (PL) treatments with doses of 3, 6 and 9 J cm -2 were applied in the 6-week storage of highbush blueberry (cv. Draper) at 0.5°C under 90-95% relative humidity (RH) conditions. The quality changes of berry were assessed by using physicochemical attributes and antioxidant activity bi-weekly at week 0, 2, 4, 6. The results show that in spite of the partial loss of the antioxidant activity and total soluble solids (TSS), PL at dose of 6 J cm -2 was found to increase firmness and titratable acidity (TA), and decrease weight loss, rot incidence, and pH during post-harvest storage, which lead to a better maintenance of blueberry quality and shelf-life extension.

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)0.0000.000
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)0.0010.000

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 head0.115
GPT teacher head0.336
Teacher spread0.221 · 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

Citations16
Published2023
Admission routes2
Has abstractyes

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