Effect of Labelling and Information on Consumer Perception of Foods Presented as 3D Printed
Bibliographic record
Abstract
Labelling and information have been shown to increase acceptance of novel food technologies. The novel technology of 3 Dimensional Printing (3DP) of foods is not well known among consumers. The study aim was to investigate the effect of the 3DP label and benefits information on consumer acceptance and perception of plausible 3DP foods. Commercially available foods, such as milk chocolate swirls, gummy candy carrots, and baked potato Smiles®, represented 3DP benefits, and each was evaluated in a sensory panel. Participants rated acceptance and perceived quality after each of three product presentations; first labeled “conventional”, then labeled “3D printed”, and again labeled 3D printed after information presentation. Participants indicated product preference after the third presentation. Food Technology Neophobia (FTN), attitude, and previous 3DP knowledge were queried. Quality rating of chocolate swirls and gummy candy carrots increased when labeled as 3DP versus conventional; information did not further increase quality ratings. Participants preferred 3DP chocolate swirls and gummy candy carrots to conventional in the final evaluation. Label and information did not change flavor, texture, or overall acceptance ratings for any product. Attitude towards 3DP of foods increased with lower FTN. Future studies could tailor information to consumer interests and knowledge gaps that highlight relevant benefits of 3DP.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".