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Record W4313894465 · doi:10.3390/foods12020296

Effect of Piperine on Saltiness Perception

2023· article· en· W4313894465 on OpenAlexafffund
Rachael Moss, Cassie Fisher, Mackenzie Gorman, Sophie Knowles, Jeanne LeBlanc, Christopher Ritchie, Kaelyn Schindell, Laurel Ettinger, Matthew B. McSweeney

Bibliographic record

VenueFoods · 2023
Typearticle
Languageen
FieldNursing
TopicBiochemical Analysis and Sensing Techniques
Canadian institutionsAcadia University
FundersNatural Sciences and Engineering Research Council of CanadaResearch Nova ScotiaCanada Foundation for InnovationAcadia University
KeywordsPiperineFood sciencePerceptionChemistryTraditional medicineMedicinePsychology

Abstract

fetched live from OpenAlex

Chemical irritants, like piperine, have the potential to increase human perception of tastes and odours, including saltiness. This cross-modal interaction could help the food industry develop new salt-reduced food products that maintain their salty taste. The objective of this study was: firstly, to determine the detection threshold of piperine (n = 72), secondly to evaluate piperine’s influence on saltiness perception in model solutions (n = 78), and lastly to identify piperine’s effect on sensory perception of low sodium soup using temporal check-all-that-apply (TCATA; n = 75). The group mean of the individual threshold was 0.55 ± 0.15 ppm. Piperine increased the saltiness perception of the model solutions, but it also increased the bitterness and decreased the sweetness of the solutions. The piperine significantly increased the saltiness intensity of the soups (evaluated using a generalized labelled magnitude), but during the TCATA task, the salty attribute was selected less for the soup with piperine than the control (based on the average proportion of selection). The TCATA indicated that the peppery attribute dominated the participants’ perception of the soup with piperine. More studies are needed to assess piperine’s cross-modal interactions.

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: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.105
Threshold uncertainty score0.188

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)0.0000.000

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 head0.011
GPT teacher head0.309
Teacher spread0.298 · 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

Citations31
Published2023
Admission routes2
Has abstractyes

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