Effects of different blowing agents on physical properties of extruded puffed snacks made from yellow pea and red lentil flours
Bibliographic record
Abstract
Abstract Pulse flours are nutritionally dense ingredients that can increase protein and fiber contents of extruded foods to create healthier snacks. However, extruding with such ingredients can deteriorate desirable physical properties such as expansion. The use of physical blowing agents (e.g., gases) can counter this. In this study, N2 and CO2 gases were used to investigate the impact of blowing agents on physical properties of red lentil and yellow pea extrudates. Microscopy imaging of extrudate cross‐sections showed increased number of cells brought about by gas injection. Some textural parameters, such as crunchiness, were positively affected by gas use regardless of pulse type, whereas others, such as bowl life, were a function of both pulse and gas type. The greatest changes in overall extrudate color and lightness were observed for red lentil with N2 gas injection. The use of physical blowing agents during food extrusion presents great potential in manipulating extrudate expansion, microstructure, texture, and color, with N2 gas well suited for red lentil extrudates in dry form and CO2 gas well suited for yellow pea extrudates in wet form. Practical applications Physical blowing agent‐assisted extrusion is a novel technology for the food industry's ability to control aerated food structure and texture. As such, the concentration and solubility of blowing agents may be manipulated to enhance the physical properties of high protein‐ and high fiber‐aerated foods, including ready‐to‐eat snacks, breakfast cereals, and gluten‐free products. The use of physical blowing agent‐assisted extrusion has tremendous potential for the development of nutritionally dense, plant‐based aerated foods with consumer appeal. The results obtained are useful for the food industry because incorporation of such food products into our daily diets, through processing them using innovative technologies, not only adds value to plants (e.g., pulses and cereals) but also has potential health, economic, and ecological benefits for the society.
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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 | 0.001 |
| 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".