A preliminary investigation into participants' reactions to a sensory trial investigating a cannabis edible
Bibliographic record
Abstract
Abstract For the food industry to successfully develop foods and beverages enriched with cannabis or cannabinoids, sensory analysis trials must be conducted. This study used open‐ended comments to evaluate a consumer acceptability trial of a cannabis edible. The participants (n = 64) were presented with an outline of a proposed consumer acceptability trial evaluating a cannabis edible and then answered questions about the sensory trial. Results suggest that conventional methodology used in sensory analyses may need to be reframed when evaluating edible cannabis products. The use of take‐home sample kits needs to be considered, and consumer acceptability trials may want to include questions about the psychoactive effects induced after consumption. As more edible cannabis products are developed, they present a unique opportunity for researchers to explore other levels of sensory characterization of these novel food products. Practical applications Sensory analysis is a key component of the food product development process, and as such, it is essential to determine how sensory methods can be best utilized to evaluate edible cannabis products. This study investigated what participants thought about a proposed consumer acceptability trial being applied to an edible cannabis product. The participants identified that (a) they wished to evaluate the full product (not a sample), (b) thought they should have been asked about the psychoactive effects, (c) wished to try the product at home and lastly, (d) did not enjoy testing the products within sensory booths. As such, researchers within the food industry working on edible cannabis products should investigate the employment of home‐use tests and questions about psychoactive effects. More studies are needed to identify which sensory methods can be best applied to evaluate edible cannabis products.
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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 | 0.013 | 0.040 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| 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".