Real-time thermal imaging of expansion dynamics during extrusion of protein-fortified snacks: Effects of nitrogen gas and protein concentration
Bibliographic record
Abstract
• Effects of nitrogen gas injection on expansion dynamics depend on protein level. • Extrudate shrinkage occurs at temperatures >100 °C for all extrudates studied. • Without gas, 0 % protein extrudate had the greatest growth time and max diameter. • Extrudate expansion varied notably across axes, especially at low protein levels. • Nitrogen injection enhanced growth time and longitudinal expansion at 50 % protein. The physical quality challenges associated with incorporating proteins into puffed snacks can be mitigated using blowing agents. This study examined the effect of nitrogen gas as a physical blowing agent, on the expansion dynamics (e.g., bubble growth and shrinkage) of corn starch-based extrudates, across a wide range of protein contents (i.e., 0–50 %, d.b.). A real-time high-speed imaging system was used to characterize extrudate expansion along different axes. Nitrogen gas injection significantly impacted the expansion behavior (i.e., expansion dynamics and expansion in different directions) of extrudates, but the effect strongly depended on the protein content in the formula. For instance, at 0 and 20 % protein, nitrogen gas injection at 150 and 300 kPa significantly (p < 0.05) enhanced the longitudinal expansion compared to extrudates produced by conventional extrusion. In addition, at 50 % protein, nitrogen gas injection at 150 and 300 kPa resulted in a significant (p < 0.05) improvement in growth time and longitudinal expansion, respectively, compared to those produced with conventional extrusion. Overall, the results underscored the potential of nitrogen gas-assisted extrusion in manipulating extrudate expansion dynamics and therefore increasing the extent of expansion, especially at high protein content. This study contributes to the field of extrusion cooking by demonstrating how advanced processing techniques can improve the quality and consumer appeal of protein-enriched snacks, offering valuable insights into optimizing extrusion processes for high-protein snack formulations.
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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".