Effect of food hydrocolloids on 3D meat-analog printing and deep-fat-frying
Bibliographic record
Abstract
Three-dimensional (3D) printing of food product is an emerging technology. This study investigated the effects of hydrocolloid addition on 3D printing of plant-protein based meat-analogs. Meat-analog inks were formulated with soy protein isolate, gluten, canola oil, and water. Hydrocolloids (xanthan gum, pectin, hydroxypropyl methylcellulose, guar gum, locust bean gum) were added to meat-analogs formulation. The influence of hydrocolloid addition and deep-fat-frying on 3D printing process parameters, thermal, structural, and physicochemical properties of meat-analogs, were investigated. Formulated inks were used to create a specific 3D cylindrical model geometry and the printed structure were subjected to deep-fat-frying (at 180°C, 90sec) in canola oil. Results showed that the meat-analog ink’s viscosity (3871-5482 Pa.s.), 3D printing rate (0.34-0.39 g.sec -1 ), printing error (2.51-10.37%), printing precision (81.97-97.27%), dimensional stability (91.22-98.61%), and cooking loss (5.69-14.23%) were significantly (p<0.05) impacted by the incorporation of hydrocolloid. Moisture-fat profile of uncooked 3D printed meat-analogs were identical, however, differences in color attributes (L*, a*, b*) among the hydrocolloids added samples were observed. Moisture, fat, and color traits of 3D printed meat-analogs were substantially impacted by deep-fat-frying. During deep-fat-frying, the loss of moisture, absorption of fat, and changes in color attributes were associated with the types of hydrocolloids incorporated in formulating the meat-analog’s ink. Overall, surface’s structure, chemical profile, and glass-transition-temperature of 3D printed deep-fat-fried meat-analogs were extremely impacted by the addition of hydrocolloids as well as by the types of used hydrocolloids in meat-analog ink. • Hydrocolloids addition impacts meat-analog ink’s viscosity • Hydrocolloid influences 3D printing rate, error, precision, dimensional stability, cooking loss • Moisture, fat, color of 3D printed deep-fat-fried analogs interwind with the type of hydrocolloid • Structural and thermal traits of fried 3D meat analogs were interlinked with types of hydrocolloid • Overall performance of hydrocolloids was printing parameter/attribute-specific
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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 | 0.000 |
| 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) | 0.001 | 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".