Effect of drying method on post-processing stability and quality of 3D printed rose-yam paste
Bibliographic record
Abstract
The addition of rose pollen to the yam paste for 3D printing could not only meet consumers' needs for nutrition and health, but also provide a new way to achieve personalized customization of healthy food. In this work, the rose-yam paste was selected as material, by comparing the post-treatment shape and color stability, bioactive substance content and hardness to explore the effect of hot air drying (HD), microwave vacuum drying (MVD) and freeze drying (FD) on the stability and quality of printed products. FD products illustrated the best post-processing stability, while HD products were the worst. FD products had a higher anthocyanin retention rate of 72.45% and a higher total phenol content of 31.33 mg/100g. However, FD products showed the lowest hardness. Although MVD products showed lower sensory score in appearance and color than FD products, their flavor score was higher than FD products, and the total sensory score of MVD products was equivalent to FD products. Moreover, MVD illustrated the highest drying efficiency, and a reduction of 84% drying time was obtained when compared with FD. Therefore, the combination of MVD and 3D printing could obtain the best quality products. This study would provide useful information on the development of post-processed high value-added 3D printed products, especially for the bio-active substances.
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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".