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Record W6959234214 · doi:10.1139/cjps-2014-245

Composition of phenolics and volatiles in strawberry cultivars and influence of preharvest hexanal treatment on their profiles

2015· article· en· W6959234214 on OpenAlexaboutno aff

Bibliographic record

VenueBioOne Complete (BioOne) · 2015
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicPostharvest Quality and Shelf Life Management
Canadian institutionsnot available
Fundersnot available
KeywordsPreharvestHexanalRipeningCultivarComposition (language)Polyphenol

Abstract

fetched live from OpenAlex

Misran, A., Padmanabhan, P., Sullivan, J. A., Khanizadeh, S. and Paliyath, G. 2015. Composition of phenolics and volatiles in strawberry cultivars and influence of preharvest hexanal treatment on their profiles. Can. J. Plant Sci. 95: 115-126. Biochemical changes of quality-determining components were evaluated in strawberry fruit subjected to preharvest spray treatments using a hexanal-containing formulation that is known to enhance shelf life and quality of fruits. Phenolic compounds and volatiles of fruits of four strawberry cultivars (Mira, Jewel, Kent, and St. Pierre) grown in southern Ontario were characterized by HPLC-MS and solid phase micro extraction (SPME) analysis. Qualitative and quantitative profiles of phenolic compounds varied among the cultivars. In all the cultivars, anthocyanins constituted the most prominent class of phenolic compounds. Volatile profiles of strawberry homogenate differed among the cultivars. Changes in phenolics and volatiles composition were determined in fruits of Mira and Jewel after spraying with a hexanal-containing formulation at weekly intervals. In Jewel, preharvest hexanal spraying altered the profiles of polyphenolic components, while minimal changes were noticed in Mira. Interestingly, very few differences were identified in ester profiles of treated and untreated Mira. In general, hexanal spray application resulted in a decrease in the abundance of several volatile components including esters, ketones, and lactones in treated Jewel compared with the control. The results suggest that cultivar-specific quality changes may result from a preharvest application of hexanal formulations, which may also imply different patterns of metabolite channeling and delay of fruit ripening processes.

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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.842
Threshold uncertainty score0.329

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 head0.369
GPT teacher head0.261
Teacher spread0.108 · 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 designObservational
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
Published2015
Admission routes1
Has abstractyes

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