Effect of food hydrocolloids on 3D meat-analog printing and deep-fat-frying
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
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
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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 it