Sensory properties of thickened and protein‐enriched plant‐based frozen desserts
Bibliographic record
Abstract
The number of consumers following plant-based diets has increased and in turn, the variety of plant-based foods available on the market has also increased. Many plant-based foods aim to mimic the functionality and sensory properties of conventional dairy products; however, they may not be suitable for specific populations. Dysphagia, for example, is a swallowing condition requiring texture-modified foods that meet specific criteria. While many conventional thickened products exist that are safe for individuals with dysphagia, the growing interest in plant-based eating alongside the increasing prevalence of dysphagia prompts a need for research on the use and safety of thickened plant-based alternatives. This study investigated the sensory properties of a thickened protein-enhanced ice cream (dairy and whey) compared to thickened protein-enhanced plant-based frozen desserts (cashew and pea, and coconut and pea). The formulations were evaluated using the International Dysphagia Diet Standardization (IDDSI) Spoon Tilt Test and a sensory trial (n = 104 participants, 47 flexitarians and 57 typical consumers) using static (hedonic scales and check-all-that-apply [CATA]), and dynamic (temporal check-all-that-apply (TCATA)) methods. The dairy and whey sample consistently passed the IDDSI test, while the plant-based samples did not. TCATA identified that the plant-based samples had an increased cohesiveness and adhesiveness, and decreased slipperiness when compared to the dairy and whey sample. The differences in textural properties may explain why the plant-based samples did not pass the IDDSI test. The study identified that although plant-based foods strive to mimic conventional dairy products, they have different textural and flavor properties.
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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) | 0.001 | 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".