Extruded Snacks from Rice, Green lentil, Chickpea and Tomato Powder Finished with Frying / Microwave Roasting
Bibliographic record
Abstract
Extrudates were first prepared through extrusion-cooking and air-drying of different formulations obtained through a D-optimal Mixture Design containing blends of rice flour (RF), green lentil flour (GL), chickpeas flour (CP), and tomato powder (TP) in different proportions. These extrudates were subjected to deep oil frying (DOF) at 200⁰C or microwave roasting (MWR) at 75% power level (1000W oven) in order to develop desirable color and flavor characteristics. Physical properties including color (L*, a*, b*), expansion ratio (ER), breaking stress (BS) and antioxidant activity (AA) were evaluated, and the influence of product variables on output parameters was assessed. Increasing CP and GL in the formulations resulted in a decrease in the ER and an increase in the BS. However, the inclusion of CP and TP helped to produce snacks of golden yellow color and increased their overall acceptability. The addition of TP also improved the antioxidant activity of the resulting product. Both DOF and MWR resulted in a lower antioxidant activity; however, MWR led to more than 80% retention of the original antioxidants and higher sensory acceptability. ER and L* values had a strong positive correlation with the overall acceptability of products. The extrusion - air-drying - microwave roasting process produced healthy snacks with acceptable sensory quality, high protein (through added pulses), and enriched antioxidant (through added tomato powder) contents.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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 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".