Impact of hydrocolloids on 3D meat analog printing and cooking
Bibliographic record
Abstract
This study investigated the effects of hydrocolloid addition on three-dimensional (3D) printing and cooking of plant ingredients-based meat analog (MA). The MA inks were formulated with soy protein isolate, wheat gluten, canola oil, water, and hydrocolloids (xanthan gum, pectin, hydroxypropyl methylcellulose, guar gum, locust bean gum) at a ratio of 22:12:15:88:2. Formulated inks were used to create a specific 3D cylindrical model geometry and the printed structure were subjected to air frying (AF:180°C, 15 min) and infrared heating (IR:180°C, 15 min). Results showed that the MA ink’s viscosity (3871–5482 Pa. s), 3D printing rate (0.34–0.39 g.sec −1 ), printing error (2.51–10.37 %), and printing precision (81.97–97.27 %) were significantly (p<0.05) impacted by the incorporation of hydrocolloids. The dimensional stability (63.17–98.58 %), and cooking loss (6.70–17.41 %) were greatly impacted by both the hydrocolloids and post-printing cooking methods. Moisture (1.71 db) and fat (0.28 db) content of uncooked 3D printed MA were identical, whereas, differences in color attributes (L value:80.21–98.88, a value:0.01–0.13, b value:0.11–2.11) among the studied hydrocolloid added samples were observed. Moisture, fat, and color traits of 3D printed meat-analogs were significantly (p<0.05) impacted by post-printing cooking methods (AF, IR). During post-printing cooking, the loss of mass (moisture, fat) and changes in color tones were associated with the types of hydrocolloids incorporated in formulating the 3D printing ink. Surfaces and internal structure, mass loss, chemical profile, and glass-transition-temperature of 3D printed meat-analogs were significantly (p<0.05) impacted by both the type of incorporated hydrocolloids and post-printing cooking methods. • Incorporation of hydrocolloids greatly influenced 3D meat-analog printing process parameters. • Physicochemical, structural, and thermal traits were associated with types of hydrocolloids. • Performance of hydrocolloids on 3D meat-analogs were attribute-specific. • Cooking methods influences surfaces structure, chemical profile, and glass-transition-temperature.
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How this classification was reachedexpand
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) | 0.000 | 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 teacher head, 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".