Comparison of<scp>3D</scp>printed and molded carrots produced with gelatin, guar gum and xanthan gum
Bibliographic record
Abstract
This study examined the effects of different hydrocolloids (guar gum, xanthan gum and gelatin) on the sensory and textural properties of pureed carrots. There were eight products involved in the study; 3D printed carrots and molded carrots without the addition of gums and with guar gum, xanthan gum and gelatin. All products were evaluated using trained panelists (n = 12) and underwent a texture profile analysis. No significant differences were found between the molded and 3D printed pureed carrots; instead, the samples were grouped based on the gum used in their production. The samples made with gelatin and xanthan gum were the hardest (texture profile analysis) and the densest samples when evaluated by the trained panelists. The 3D printing did not affect the taste properties of the pureed carrots, as they were evaluated to be similar to that of the molded carrots (p > .05). This study demonstrated that 3D printing did not affect the textural and sensory properties of pureed carrots when compared to molded carrots. However, changes in the printing parameters (infill percentage, nozzle diameter, flow rate, nozzle height) need to be evaluated to determine their effect on the sensory properties of 3D printed pureed carrots.
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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 | 0.000 | 0.001 |
| 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) | 0.001 | 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".