Effect of Labelling and Information on Consumer Perception of Foods Presented as 3D Printed
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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) | 0.004 | 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 it