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The Effect of Frequency of Consumption of Artificial Sweeteners on Sweetness Liking by Women

2007· article· en· W2128144515 on OpenAlexaff
Alyson Mahar, Lisa M. Duizer

Bibliographic record

VenueJournal of Food Science · 2007
Typearticle
Languageen
FieldNursing
TopicBiochemical Analysis and Sensing Techniques
Canadian institutionsAcadia University
Fundersnot available
KeywordsSweetnessFood scienceArtificial SweetenerAspartameSugarAdded sugarSucraloseOrange juiceSucroseTasteChemistry

Abstract

fetched live from OpenAlex

Research into sweetness perception and preference thus far has demonstrated that sweetness preference is related not to the total sugar consumed by an individual but the amount of refined sugar ingested. Research has yet to be conducted, however, to determine whether a diet high in artificial sweeteners contributes to sweetness liking and preference with the same result as a diet high in sugar. The purpose of this research was to determine if such a relationship exists with regard to diets high in artificially sweetened beverages. Seventy-one female participants were recruited and screened for sweetener consumption in beverages. Sixty-four of these individuals were selected for sensory testing. All participants evaluated orange juice samples (ranging from 0% added sucrose to 20% added sucrose) for liking of sweetness using a 9-point hedonic scale. Based on screening survey data, participants were categorized according to sweetener consumption group (artificial sweetener consumers and natural sweetener consumers) and by overall sweetened beverage intake (low or high, regardless of sweetener type normally consumed). Sensory data were analyzed to compare sweetness liking in each of these groups. Significant differences in liking were observed, with individuals in the high sweetened beverage intake group preferring sweeter orange juice than those in the low-intake group. Categorization by sweetener type resulted in no significant differences between the groups, indicating that regardless of the type of sweetener consumed in a beverage, liking of sweetness will be influenced in the same manner.

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: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.018
Threshold uncertainty score0.276

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 studies0.0000.001
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 head0.015
GPT teacher head0.292
Teacher spread0.277 · 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

Citations47
Published2007
Admission routes1
Has abstractyes

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