Industrial processing of chickpeas ( <i>Cicer arietinum</i> ) for protein production
Bibliographic record
Abstract
Abstract The increasing global interest in plant‐based proteins stems from concerns about the environmental impact, sustainability, animal welfare, and health implications associated with consuming animal‐based proteins. In the frame of alternative protein sources, chickpea ( Cicer arietinum ) emerged as a rich source of dietary proteins besides containing good amount of carbohydrate, fat, and fiber. As a protein ingredient, chickpea is available in three forms, namely, flour, concentrate, and isolate. This chickpea protein can be extracted using both wet and dry fractionation methods where the former one includes wet extraction followed by isoelectric precipitation, while the later one indicates dry milling followed by air classification. However, different nonthermal emerging technologies have been seen to assist in extracting protein as well as modifying their functionalities. This review gives an outline of the recently available literature on composition, industrial processing and associated technological challenges, functionality, and application of chickpea protein ingredients. Furthermore, discussion on the modification/improvement of chickpea protein functionality with the assistance of emerging technologies and the potentiality of by‐products produced during chickpea protein processing are also included. Based on the available findings and discussion, it is seen that apart from being a comparable source of alternative animal‐based protein to extract, chickpea derived by‐products can also be a potential source of valued ingredients that might contribute to the circular economy.
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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 | 0.001 | 0.001 |
| 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".