Sensory properties of thickened tomato soup enhanced with different sources of protein (whey, soy, hemp, and pea)
Bibliographic record
Abstract
Thickened soup formulations were created with different proteins (hemp, soy, pea, and whey) to improve protein and fluid intake. The formulations consisted of a control soup, and soups with 6% whey protein, 6% hemp protein, 6% pea protein, and 6% soy protein by volume. The suitability of the samples for those living with dysphagia was evaluated using the international dysphagia diet standardization initiative (IDDSI) spoon tilt test and a sensory trial (51 older adults and 51 younger adults). The sensory trial used nine-point hedonic scales and check-all-that-apply to evaluate the different formulations. The sample with the whey addition was not significantly different than the control in terms of liking of flavor and texture, but it decreased the participants' overall liking. The hemp, pea, and soy decreased overall liking as well as liking of flavor and texture. They were associated with off-flavors, aftertaste, and astringency. The responses from the older and younger adults were compared and significant differences were found in their liking of the texture, with the older adults finding the formulations' texture significantly more acceptable. Overall, the study identified that hemp, pea, and soy did not create acceptable thickened soup formulations and the hemp and pea formulations did not achieve a consistency level that is acceptable for those living with dysphagia.
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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 | 0.000 |
| 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".