A Prospective Review of the Sensory Properties of Plant-Based Dairy and Meat Alternatives with a Focus on Texture
Bibliographic record
Abstract
Consumers are interested in plant-based alternatives (PBAs) to dairy and meat products, and as such, the food industry is responding by developing a variety of different plant-based food items. For these products to be successful, their textural properties must be acceptable to consumers. These textural properties need to be thoroughly investigated using different sensory methodologies to ensure consumer satisfaction. This review paper aims to summarize the various textural properties of PBAs, as well as to discuss the sensory methodologies that can be used in future studies of PBAs. PBAs to meat have been formulated using a variety of production technologies, but these products still have textural properties that differ from animal-based products. Most dairy and meat alternatives attempt to mimic their conventional counterparts, yet sensory trials rarely compare the PBAs to their meat or dairy counterparts. While most studies rely on consumers to investigate the acceptability of their products' textural properties, future studies should include dynamic sensory methodologies, and attribute diagnostics questions to help product developers characterize the key sensory properties of their products. Studies should also indicate whether the product is meant to mimic a conventional product and should define the target consumer segment (ex. flexitarian, vegan) for the product. The importance of textural properties to PBAs is repeatedly mentioned in the literature and thus should be thoroughly investigated using robust sensory methodologies.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".