An investigation into consumer perception of the aftertaste of plant-based dairy alternatives using a word association task
Bibliographic record
Abstract
Plant-based alternatives are a growing market segment, but they have been found to be associated with different flavours, textures, and aftertastes than conventional dairy products. The aim of this study was to evaluate consumer perception of plant-based beverages’ (PBBs) and plant-based cheeses’ (PBCs) aftertaste. Two sensory trials were conducted: one investigating PBBs (n=104) and the other PBCs (n=109). In both trials, five different samples (PBBs or PBCs) were evaluated using nine-point hedonic scales, intensity scales and a word association task. The participants were asked to provide the first four words or phrases that described the aftertaste of each sample during the word association task. The results found that as the aftertaste intensity increased, the participant's overall liking of the food product decreased. Consumers preferred plant-based alternatives to have an aftertaste that mimics conventional dairy products. Consumers also identified mouthcoating and textural properties when describing the aftertaste of PBBs and PBCs. Lastly, a strong and lingering aftertaste was disliked by the consumers, while PBBs and PBCs with a quick and mild aftertaste were preferred.
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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 | 0.003 |
| 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 | 0.001 | 0.001 |
| 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".