Microstructure dynamics-texture relationships in plant protein-fortified puffed snacks: Insights into gas-assisted extrusion cooking
Bibliographic record
Abstract
• Effects of nitrogen gas injection on extrudate structure depend on protein level • Extrudates with larger cells tend to have thicker cell walls and are less uniform • Extrudate hardness is positively correlated with maximum cell wall thickness • Both extrudate crunchiness and crispness show positive correlations with cell size • Nitrogen gas injection’s positive effects were most evident at 50% protein level Microstructure is critical to an appealing texture, with expansion dynamics playing central roles in shaping extrudate cellular structure. Nitrogen gas-assisted extrusion has emerged as a novel sustainable technique for enhancing the quality of high-protein extruded snacks. Yet, it has not been comprehensively studied, particularly its effects across varying protein levels. In light of this gap, the microstructure of extrudates produced by varying nitrogen gas injection pressures and a broad range of protein content was systematically quantified using X-ray microtomography. Moreover, the correlations among extrudate expansion dynamics, microstructure, and physical properties were examined. Overall, radial expansion promoted cell growth, resulting in extrudates with bigger cells, thicker cell walls and less uniform cell structure. A significant positive correlation was observed between extrudate hardness and maximum cell wall thickness (r = 0.55, p < 0.05). Additionally, cell size significantly correlated positively with crunchiness (r = 0.64, p < 0.05) and crispness (r = 0.80, p < 0.001). The beneficial effects of nitrogen gas injection were most pronounced at 50% protein, characterized by reduced density, cell wall thickness, hardness, and enhanced microstructure uniformity. Findings highlighted nitrogen gas-assisted extrusion as a potentially novel sustainable way for producing appealing high-protein snacks, offering more nutritious plant-based food options.
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.
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 | 0.001 |
| 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".