Sensory evaluation of plant-based cheese: a systematic review with a focus on texture and mouthfeel
Bibliographic record
Abstract
Consumer interest in plant-based cheese is rising due to environmental, animal welfare, and health concerns, but texture remains a major challenge in replicating the creaminess, smoothness, and meltability of dairy cheese. The absence of casein in plant-based cheese often results in brittle or gummy textures, whereas plant-based fats and proteins often lead to phase separation and weak cohesion, thereby affecting sensory quality. While instrumental texture analysis is common in the lietarature, many studies lack a correlation between instrumental measures, sensory perception and consumer acceptance, limiting practical relevance. Against this background, this review systematically examined 85 sensory studies on plant-based cheese focusing on texture modification, sensory evaluation, and consumer response. Second, the review assessed the standardization of texture and mouthfeel terminology. Only four studies provided clear definitions of texture attributes, underscoring the need for greater standardization across studies. Third, it evaluated the quality of sensory and consumer analyses within these studies. A total of 60% of the studies (51 out of 85) had methodological issues, including small sample sizes and poorly reported sensory evaluation methods, highlighting key gaps in sensory studies on plant-based cheese. Future studies must refine sensory and consumer evaluation techniques to better replicate the dairy-like texture and mouthfeel in order and meet consumer expectations.
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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 | 0.003 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.007 | 0.006 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".