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USE OF AN ELECTRONIC NOSE TO STUDY THE CONTRIBUTION OF VOLATILES TO ORANGE JUICE FLAVOR

2002· article· en· W2088461874 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.

Bibliographic record

VenueJournal of Food Quality · 2002
Typearticle
Languageen
FieldEngineering
TopicAdvanced Chemical Sensor Technologies
Canadian institutionsAgriculture and Agri-Food Canada
Fundersnot available
KeywordsOrange juiceChemistryOrange (colour)FlavorElectronic noseFood scienceLimoneneFruit juiceChromatographyEssential oilBiology

Abstract

fetched live from OpenAlex

Abstract Ideal orange juice and orange juice from which the volatile compounds had been stripped were analyzed using an electronic nose. In the ideal juice, dlimonene was the volatile in highest concentration. The stripped orange juice contained less than 1% of the original d‐limonene and all other volatiles were reduced to below levels of detection as measured by gas‐chromatography analysis. The electronic nose was able to distinguish between the two orange juices, ideal and volatile stripped. Four commercial orange juice essences were added to the stripped orange juice at concentrations up to 2%. The differences, as measured by the electronic nose, between the original orange juice and the volatile stripped orange juice were reduced when orange essence was added to the volatile stripped juice. In orange juice flavor, α‐pinene, sabinene, β‐myrcene, and d‐limonene appear to play an important role.

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)0.001
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 score0.012
Threshold uncertainty score0.280

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
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 head0.061
GPT teacher head0.310
Teacher spread0.249 · 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