Investigation into the Sensory Properties of Plant-Based Eggs, as Well as Acceptance, Emotional Response, and Use
Bibliographic record
Abstract
Consumers have become interested in plant-based alternatives to animal-based products. One of the under-studied alternatives is plant-based eggs (PBEs). This research investigated PBEs relative to conventional eggs and tofu scramble-another plant-based alternative. Firstly, participants (n = 93) completed a word association task asking them about PBEs. Participants then evaluated the different food samples using hedonic scales, check-all-that-apply (CATA), and temporal check-all-that-apply (TCATA), as well as identified their emotional response and proposed use for PBEs. Participants were interested in plant-based alternatives, including PBEs, but they were concerned about the sensory properties. When they evaluated the different samples, the flavour and texture of the PBEs were disliked in comparison to the eggs. This result may be due to the beany, bitterness, and off-flavour attributes associated with the PBEs. Participants also associated the PBEs with negative emotions. The liking of tofu scramble was not significantly different from the eggs, and the eggs and tofu scramble were mainly associated with positive emotions. During the TCATA evaluation, the participants focused on the flavour attributes of PBEs, while their evaluation of the eggs was dominated by the textural attributes. Whether following a plant-based diet or not, consumers are interested in PBEs, but the sensory properties of PBEs need to be improved before they are willing to adopt them into their diet. This study is one of the first to evaluate the sensory properties of PBEs, as well as consumers' emotional response to them and their attitudes about PBEs.
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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 | 0.002 |
| 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".