Effect of pretreatment methods of Kabuli chickpea on microstructure and physical properties of enriched bread
Bibliographic record
Abstract
This study examines the impact of chickpea pretreatment methods on the microstructural and physical properties of chickpea-enriched bread, with the goal of determining the most effective treatment for improving bread quality. Five bread formulations were analyzed: a control (100 % wheat flour), a sample with 20 % untreated chickpea flour, and three samples containing 20 % chickpea flour derived from germinated, roasted, or micronized chickpeas. The microstructure of the breads was analyzed using X-ray microcomputed tomography, whereas the physical properties (bread weight, specific volume and crumb colour) were measured according to standard AACC methods. Germination and micronization of chickpeas statistically significantly increased the protein content of the resulting flours (22.1 and 21.5 % dry basis, respectively) as compared to untreated chickpea flour (20.8 % dry basis). The inclusion of pretreated chickpea flours enhanced bread weight and reduced specific volume relative to untreated chickpea-enriched bread and the control sample. Microstructural analysis of breads made with flour from roasted and germinated chickpeas depicted increased open pores, greater uniformity in porosity distribution, thinner crumb walls, and a well-defined, interconnected 3D structure. In conclusion, germination is a promising pretreatment that can be used on chickpeas to enhance the nutritional quality of bread, ensuring physical integrity while enhancing microstructural properties. This study provides valuable insights for optimizing bread production through the incorporation of chickpea flour to develop nutritionally enriched bread. • Specific volume of untreated and pretreated chickpea-enriched breads was statistically significantly differenet. • Germination enhances key microstructural traits in chickpea-enriched bread. • Principal component analysis showed that pore volume correlates with bread weight.
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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 | 0.000 |
| 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) | 0.000 | 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".