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Record W2765341748 · doi:10.1002/9781119158042.ch24

Flavors and Aromas: Chemistry and Biological Functions

2017· other· en· W2765341748 on OpenAlexaff
Charles F. Forney, Jun Song

Bibliographic record

Venuenot available
Typeother
Languageen
FieldAgricultural and Biological Sciences
TopicPostharvest Quality and Shelf Life Management
Canadian institutionsAgriculture and Agri-Food Canada
Fundersnot available
KeywordsAromaFlavorChemistryPostharvestFood scienceOrganic chemistryBotanyBiology

Abstract

fetched live from OpenAlex

Flavor and aroma are important characteristics that contribute to the quality of fresh fruits and vegetables. This chapter discusses the chemistry responsible for the flavor and aroma of fresh fruits and vegetable, explores the biochemical mechanisms by which they are formed, and identifies factors that affect flavor both pre- and postharvest. The chemical constituents of fruits and vegetables that provide the stimulus resulting in the perception of flavor and aroma are diverse. The volatile compounds that determine aroma and contribute to the flavor of fruits and vegetables are found in low concentrations, rarely exceeding 30 ppm, and the concentration of individual volatile compounds can vary widely. Esters contribute fruity aromas and flavors. Esters are formed by the esterification of carboxylic acid derivatives and alcohols. A variety of metabolic pathways have been shown to be involved in the synthesis of aroma and flavor compounds in fruits and vegetables.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.071
Threshold uncertainty score0.997

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)0.0030.000

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 head0.036
GPT teacher head0.232
Teacher spread0.195 · 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.

Study designNot applicable
Domainnot available
GenreOther

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

Citations19
Published2017
Admission routes1
Has abstractyes

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