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Record W1598475291 · doi:10.1111/1750-3841.12252

Sensory Evaluation of Dry‐fermented Sausage Containing Ground Deodorized Yellow Mustard

2013· article· en· W1598475291 on OpenAlexafffund
Shuliu Li, Michel Aliani, Richard A. Holley

Bibliographic record

VenueJournal of Food Science · 2013
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicMeat and Animal Product Quality
Canadian institutionsUniversity of Manitoba
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsFood scienceStarterFlavorChemistryFermentationMustard seedSensory analysis

Abstract

fetched live from OpenAlex

Ground deodorized yellow mustard is used as a binder and meat protein substitute in cooked processed meat products. Recent studies have shown that it has the potential to be used in uncooked processed meat products because of its natural antimicrobial properties. In the present study, ground deodorized yellow mustard was added to uncooked dry-fermented sausage during manufacture at 1% to 4% (w/w) and analyzed for its effects on starter cultures, physico-chemical properties, and consumer acceptability. Mustard had a nondose-dependent inhibitory effect on the Staphylococcus starter culture, had no effect on water activity or instrumental texture, and tended to accelerate sausage pH reduction. At 3% and 4% mustard, consumer scores on all sensory attributes as well as overall acceptability were significantly lower. The appearance and color of 3% and 4% mustard-treated sausages were liked slightly, whereas flavor, texture, and overall acceptability scores were reduced. The control without mustard and 1% mustard-treated sausages had similar sensory properties and were the most acceptable, while 2% mustard-treated sausages were given "like moderately" and "like slightly" descriptors. Sensory results mean that at concentrations necessary for mandated regulatory control of Escherichia coli O157:H7 in dry sausages, mustard may have a negative effect on consumer acceptance.

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)0.005
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.704
Threshold uncertainty score0.420

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0050.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
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 head0.121
GPT teacher head0.308
Teacher spread0.187 · 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

Citations5
Published2013
Admission routes2
Has abstractyes

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