The Cross‐Modal Interaction of White Pepper Included in a Bread Formulation: An Investigation Using Hedonic Scales and Rate‐All‐That‐Apply
Bibliographic record
Abstract
ABSTRACT White pepper is a chemical irritant identified to increase saltiness perception in a liquid food matrix (soup). However, it also leads to off‐flavors and bitterness in soup. The objective of this study was to evaluate the feasibility of using white pepper in a solid food matrix (bread) to influence saltiness perception. Furthermore, consumer acceptability and sensory perception were investigated. The bread was made with 0.2% (0.2WP), 0.4% (0.4WP), and 0.6% (0.6WP) on a weight per weight basis with the flour. Also, a control bread without white pepper was made. Consumers ( n = 84) evaluated the bread for liking using hedonic scales and their sensory perception using rate‐all‐that‐apply. The 0.2WP sample was liked significantly more than the control. Also, the flavor of the 0.2WP and 0.4WP samples was liked more than the control. The 0.6WP was not significantly different from the control. The 0.2WP and 0.4WP had higher saltiness perception than the control, while the 0.6WP was found to be spicier and increased bitterness in comparison to the control. The addition of the white pepper also impacted the textural perception, with an increasing amount of white pepper the bread was perceived to be harder. Overall, this study identified how white pepper can be used in a solid food matrix to enhance saltiness perception.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".