Functional and thermal properties of yellow pea and red lentil extrudates produced by nitrogen gas injection assisted extrusion cooking
Bibliographic record
Abstract
Abstract BACKGROUND There are excellent opportunities for greater incorporation into our diets of pulses, which are rich in proteins and dietary fibers, if their functional properties are modified to fit a wide range of applications in the food industry. The objective of this research was to produce high protein and fiber extrudates from yellow pea and red lentil flours using conventional and N2 gas injection assisted extrusion cooking methods. The effects of process variables on extrudate functional and thermal properties were also investigated. RESULTS The cold viscosity of extrudates produced by N2 gas injection were higher than those produced by conventional extrusion, indicating that gas‐assisted extrusion does affect the end‐product pasting properties. At higher barrel temperatures (150–175 °C) extrudates did not exhibit any thermal transition in their thermograms, and thus their starches were completely gelatinized and proteins completely denatured during extrusion. In general, water solubility of extrudates produced by N2 gas injection was significantly (P < 0.05) higher than those produced by conventional extrusion. Emulsion capacity and stability of yellow pea extrudates were in the range of 44–50% and 42–47%, respectively, and the counterpart values of red lentil extrudates were very similar (in the range of 43–47% and 43–46%, respectively). CONCLUSION Nitrogen gas injection assisted extrusion cooking can be used practically in development of pulse extrudates which contain high protein and dietary fiber. This novel and innovative technique is a reliable alternative method to the conventional CO2 gas injection assisted extrusion cooking methods in the snack food and food ingredient industries. © 2019 Society of Chemical Industry
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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".