Temporal Sensory Profiles of Regular and Sodium-Reduced Foods Elicited by Temporal Dominance of Sensations (TDS) and Temporal Check-All-That-Apply (TCATA)
Bibliographic record
Abstract
Temporal sensory methods can be used to highlight the impact of sodium reduction on the dynamic sensory profile of foods targeted for sodium reduction. Study aims were to compare the temporal sensory attribute profiles of regular and sodium-reduced food products elicited by TDS and TCATA, over single and multiple oral intakes. A total of 20 semi-trained participants evaluated commercially available regular and sodium-reduced canned corn, cooked ham (single intakes), potato chips and cream of mushroom soup (5 intakes) using both TDS and TCATA. Regular and sodium-reduced products differed in not only salty but also other sensory attributes, noticeably dry for chips, sweet for corn, bitter and metallic for ham, thick, creamy, sweet, and starchy for soup. TDS and TCATA provided comparable information for the key sensory attributes characterizing and differentiating the regular and sodium-reduced products. TDS profiled significant differences between samples for a larger number of attributes than TCATA, while TCATA profiles were more consistent across intakes. Multiple intakes changed the duration of attribute dominance but not the number of significantly dominant attributes in TDS profiles. The current findings provide insight for applications of temporal profiling to other food products and development of sodium-reduced foods with attribute profiles acceptable to consumers.
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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) | 0.002 | 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".