Physicochemical, microstructural, and functional properties of <i>Cicer arietinum</i> okara flour–a chickpea beverage by-product
Bibliographic record
Abstract
Abstract This study investigated the physicochemical, microscopical, and functional properties of chickpea (Cicer arietinum) okara flours. The flours were prepared from chickpea okara obtained as a by-product following the preparation of chickpea beverages using conventional, microwave, and ultrasound processing. The assessment of the okara flours focused on evaluating the influence of the processing methods on their physicochemical, functional, and microstructural characteristics. Through comprehensive analyses, the study examined how the different processing techniques affected the composition and properties of the resulting okara flours. Furthermore, the study included a comparative mass balance analysis to assess the extraction efficiency of the three processing methods. The findings revealed significant variations in the composition and properties of the okara flours among the different processing methods. Each method exhibited unique effects on the physicochemical, functional, and microstructural attributes of the resulting flours. Consequently, it was not possible to identify a single “best” processing method for obtaining optimal okara flour characteristics since all flours had interesting and unique properties. Overall, this study provides valuable insights into the effects of different processing methods on chickpea okara flour. The findings highlight the importance of selecting an appropriate processing technique based on the desired properties and applications of the flour. The results can contribute to the development of tailored processing approaches for enhancing the utilisation of chickpea okara flour in various food formulations, thereby promoting sustainability and reducing waste in the food 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".