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Record W4388539791 · doi:10.1093/jas/skad281.542

PSVIII-12 Hexanal, as Identified Through Flash Gas Chromatography Electronic Nose, Strongly Correlates with Pv in Rendered Chicken Meal Samples, Although Additional Compounds Influence Aromatic Palatability

2023· article· en· W4388539791 on OpenAlexaboutno aff
Fiona B McCracken, Jason C. Fowler, C. Timlin, Lili Towa, Sarah M Dickerson, C.N. Coon

Bibliographic record

VenueJournal of Animal Science · 2023
Typearticle
Languageen
FieldEngineering
TopicAdvanced Chemical Sensor Technologies
Canadian institutionsnot available
Fundersnot available
KeywordsHexanalChemistryPalatabilityChromatographyElectronic noseOdorMealFood scienceAnimal scienceBiologyOrganic chemistry

Abstract

fetched live from OpenAlex

Abstract Samples of rendered chicken meal (CM) and chicken by-product meal (CB) were obtained from a commercial plant with samples representing various storage time points and a range of peroxide values from < 10 (1), 20-50 (2), 50-100 (3), 100-200 (4). Samples were utilized in a novel aromatic palatability trial with 20 working Labrador Retrievers (n = 10 males and n = 10 females). Aggregated interaction time showed the most pronounced difference between the lowest PV sample and the highest PV for both CB and CM (P < 0.0001, 0.001). Both CB1 and CM1 were significantly different from all other samples (P = 0.05). Samples were also sent to a third party for HERACLES Neo flash gas chromatography electronic nose analysis (e-nose). Analysis of e-nose chromatograms showed good discrimination between samples (DI = 97) with the largest spatial differences between the low and high PV samples. Chromatogram peaks were tentatively identified according to their Kovats index and the AlphaChemBase database. Component 1 (X-axis) accounted for 99.4% of the difference while component 2 (Y-axis) showed 0.54% of the difference. The strongest odor, as determined by the highest area peak was hexanal which produces a fatty and fishy smell. When evaluated in a bivariate model hexanal vs PV was well correlated on a 2P curve (R2= 0.986, P < 0.0001). However, hexanal was less correlated with aromatic palatability (R2 = 0.616. P = 0.09). Therefore, a primary least squares model was created to better display the relationship between palatability and oxidation. Utilizing two factors, the PLSR model explained 72% of the variation in the e-nose peak area data (X) and 90% of the variation in the PV*palatability (Y). Of the 61 aromatic compounds identified, the model identified 45 compounds with a VIP over 0.8. Both PV and palatability were well-fitted by the model as indicated by their position between the outer two ellipses (R2 = 75% to 100%). In the model, Hexanal had a VIP of 1.1 suggesting it was an important component, although other compounds had significantly better VIP scores and are likely to better predict palatability. Therefore, these data show aromatic hexanal is an accurate predictor of the oxidation of a product, although it may not provide the full picture regarding palatability.

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: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.732
Threshold uncertainty score0.878

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.003
Science and technology studies0.0000.001
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0000.001
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 head0.015
GPT teacher head0.251
Teacher spread0.236 · 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 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

Citations0
Published2023
Admission routes1
Has abstractyes

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