Oxidizing agent‐assisted extrusion cooking of yellow peas and the techno‐functionality of the resulting extrudate flours
Bibliographic record
Abstract
To enhance pulse flour techno-functionality, different oxidizing agents were used during extrusion cooking. Benzoyl peroxide (BP) (45 mg/kg), azodicarbonamide (ADA) (150 mg/kg), and pressurized air (injection pressures of 200 and 400 kPa) were employed at three different extrusion temperature profiles, and their effects on techno-functional quality of resulting yellow pea (YP) extrudate flours were investigated. Oxidizing agents and extrusion temperature impacted water solubility (WS), water-binding capacity, emulsion capacity (EC), emulsion stability (ES), and pasting properties of YP extrudate flours. Oxidizing agent effects were extrusion temperature-dependent. At a die temperature of 95℃, BP and ADA addition significantly increased EC and ES, while air injection at 400 kPa increased WS, ES, cold, and trough viscosities. Manipulation of pulse flour techno-functionality through oxidizing agent-assisted extrusion has proven to be an effective approach to manufacture novel ingredients that can be used in a wide variety of foods. Practical applications High protein and fiber content of pulses make them an attractive ingredient for new product development strategies in the food industry. Nevertheless, there can be significant quality challenges when using pulses in food products due to their less than ideal techno-functional properties which lead to quality defects in reformulated products. Extrusion cooking has been employed to modify pulse flours and to develop pulse-based ingredients with superior techno-functionality. The use of oxidizing agents during extrusion cooking can be another means of addressing these techno-functionality issues, without the need of additional equipment for the industry. In this study, depending on the oxidizer type and extrusion temperature, oxidizing agent-assisted extrusion cooking has proven to improve several techno-functional quality attributes of yellow pea flour. The same technology can be employed to improve the techno-functionality of different pulse flours.
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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 | 0.001 |
| 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".