Piperine’s impact on sensory perception in different food matrices (liquid and solid)
Bibliographic record
Abstract
Cross-modal interactions of chemical irritants are of interest to the food industry, especially if they can increase saltiness perception. Piperine’s (the primary alkaloid found in black pepper) cross-modal interactions and influence on sensory perception needs to be further explored. As such, the objective of the study was to add piperine at both the detection threshold (0.55 ppm), and below detection threshold (0.50 ppm), to three different food matrices (curried rice, tofu, and tomato soup). Participants (n = 95) evaluated the different food items using hedonic scales (nine-point) and intensity scales (15 cm). The piperine addition at detection threshold increased the participants' liking of the flavour, texture, and overall liking of the curried rice when compared to piperine added below detection. The different concentrations of piperine did not impact the liking of the other food items. The food matrices impacted the perception of spiciness, but did not impact the taste intensities. However, the taste intensities were impacted by the different concentrations of piperine. Piperine at detection threshold increased the saltiness perception of the tomato soup and tofu, as well as the bitterness of the curried rice and tomato soup. Furthermore, the participants were categorized based on spicy food consumption, and although it did not impact overall liking, infrequent consumers (n = 36) scored the foods with piperine added at detection threshold higher for spiciness than frequent consumers (n = 59). More studies are needed to assess piperine’s cross-modal interactions and to specifically identify how piperine's cross-modal interactions are impacted by different components of food (e.g. fat, starch, etc.).
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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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".