Impact of fat level on piperine’s cross-modal interaction on saltiness perception
Bibliographic record
Abstract
Novel strategies are needed to reduce the salt content of different food products. Chemical irritants, such as piperine, have been proposed to have a cross-modal interaction that can enhance saltiness perception. The objective was to add piperine to a soup below the detection threshold (0.50 ppm) and identify its’ impact on sensory perception. The study also identified how increasing the fat content in the soup impacted the sensory properties of the soup. For this experiment, cream of potato soup was made without piperine (control), along with soups with piperine (below detection threshold) and differing amounts of fat (four different samples, 0 %C, 50 %C, 75 %C and 100 %C). Participants (n = 89) evaluated the soup using intensity scales (15 cm) and nine-point hedonic scales. The piperine led to an increased saltiness in the soup, as well as bitterness, spiciness, and a decreased liking. The fat addition increased the liking of the soup and reduced the bitterness and spiciness. It also increased sweetness and aftertaste intensity. However, the fat addition did not impact the saltiness perception. The participants were categorized based on their preference level for spice. Those that had a greater spice preference rated the samples more positively and noticed the spiciness in the samples less than those that had a lesser spice preference. Further studies are needed to continue assessing piperine’s cross-modal interaction in different food matrices.
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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 | 0.000 | 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".