Green extraction of chickpea (Cicer arietinum)-based functional beverage: Assessment of nutritional quality and storage stability
Bibliographic record
Abstract
Microwave and ultrasound were investigated for their ability to enhance the nutritional content in chickpea ( Cicer arietinum )-based functional beverage extraction, and the obtained results were compared to conventional beverage processing. This was followed by the storage stability analyses in terms of changes in physicochemical (pH, TSS, color), microbial, and functional (total phenolic content, total flavonoid content, antioxidant activity) properties for 21 days. The findings of our study indicate that ultrasound and microwave processing techniques exhibit distinct advantages over conventional processing in terms of not only enhancing the protein yield but also retaining the bioactive and functional compounds during storage. Among the extraction methods, microwave was found more effective with respect to higher protein yield (7.89 ± 0.11 g/100 g of beverage), protein solubility (18.54% ± 0.23%), and in vitro protein digestibility (90.79% ± 0.64%) at the optimum conditions (temperature: 65.8 °C and time: 6.7 min). Regarding storage stability, microwave processing led to a remarkable increase of 47.32% in total phenolic content and 58.34% in total flavonoid content. The microbial study revealed less total bacterial and fungal counts in microwave and ultrasonically processed beverages over the storage period. Moreover, the study also revealed that the processing treatments applied did not result in significant alterations in the pH and acidity values of the chickpea beverage.
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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".