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Record W2031357466 · doi:10.4141/cjps07170

Flavour volatile production and regulation in fruit

2008· article· en· W2031357466 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.
venuePublished in a venue whose home country is Canada.

Bibliographic record

VenueCanadian Journal of Plant Science · 2008
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicPostharvest Quality and Shelf Life Management
Canadian institutionsAgriculture and Agri-Food Canada
FundersAgriculture and Agri-Food Canada
KeywordsFlavourRipeningPostharvestAromaCucumisFood scienceBiologyFlavorMalusFruit juiceBiotechnologyHorticulture

Abstract

fetched live from OpenAlex

Consumption of fresh fruits is increasing as consumers become more aware of their nutritional value and role in disease prevention. Improving the flavour properties of fresh fruit reaching the consumer would add value, increase consumption, and create new markets for these commodities. One of the important characteristics of fruit ripening is volatile aroma production. Using apples (Malus domestica), strawberries (Fragaria ananassa Duch.) and melons (Cucumis melo L. var. reticulatus Naud; C. melo L. inodorus; C. melo L. cantalupensis) fruit as examples, this review focuses on recent developments in fruit aroma research. Both sensory studies and instrumental analysis confirm the importance of volatile production in fruit and its contribution to eating quality. Sensory analysis should further define the contribution of individual volatile compounds to total flavour quality. Volatile biosynthesis and its contribution to fruit eating quality is very complex, and is influenced by many factors, such as genome, harvest maturity, and postharvest handling and storage. Application of 1-methylcyclopropene (1-MCP) and transgenic lines suppressing ethylene action and biosynthesis demonstrate ethylene involvement in volatile formation and provide useful research tools to elucidate the volatile production process during fruit ripening. Cloning of genes and characterization of enzymes responsible for volatile production helps us to understand the biochemical pathways and control mechanisms. Despite the exciting developments in flavour research, several challenges still remain to be solved. An understanding of the fundamental mechanisms controlling changes in flavour quality is limited, and most biochemical pathways determining this quality trait are still unknown. As part of secondary metabolism, volatile production in fruit is a complex process with tightly controlled systems involving substrates, enzymes and energy from many pathways. It is our hope that the biochemical pathways regulating the synthesis of volatile compounds in fruit will be determined using integrated approaches, including biochemical, genomic, proteomic and microscopy tools to determine fundamental metabolism and its localization. Combining these efforts with direct measurement of sensory properties will lead us to new methods to optimize and retain fruit flavour in the market place. Key words: aroma, fruit ripening, volatile analysis, sensory, apple, strawberry, muskmelon

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)0.001
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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.210
Threshold uncertainty score0.887

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 head0.039
GPT teacher head0.204
Teacher spread0.166 · 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