Effect of tempering moisture and infrared heating temperature on the functionality of Desi chickpea and hull‐less barley flours
Bibliographic record
Abstract
Background and objectives The effect of seed tempering moisture (20% moisture content or left un‐tempered) and infrared heating surface temperature (115 or 135°C) on the functional properties of Desi chickpea and hull‐less barley was investigated. Findings Tempering and infrared heating reduced the protein solubility in both flours, whereas the ability to bind oil was unaffected. Both flours had increased water binding abilities in response to tempering and infrared heating. In the case of chickpea flour, the emulsion activity (EA) increased when the seeds were tempered and heated to 135°C, whereas both emulsion and foaming stability remained unchanged with all processing conditions. Tempering before infrared heating decreased the foaming capacity of chickpea flour. In contrast, barley flour showed a decrease in both EA and stability, and became nonfoaming with infrared heating with and without tempering. Conclusions Infrared heating has both positive and negative effects on the functional properties of Desi chickpea and hull‐less barley flours. Significance and novelty Findings from this work will help direct ingredient processors to process seeds to achieve different functionalities which is important for food product development purposes.
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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".