Optimization of the effect of cold plasma treatment on UAE-NADES green extraction of chickpea roots (Cicer arietinum) bioactive compounds
Bibliographic record
Abstract
The chickpea (Cicer arietinum L.) root is an agricultural by-product with the potential for extracting valuable bioactive compounds that often remains underutilized. This study introduces an integrated extraction methodology to enhance the extraction of bioactives using atmospheric air low-pressure cold plasma (CP) treatment followed by ultrasound-assisted extraction (UAE) with natural deep eutectic solvents (NADES). Chickpea root powder was first subjected to CP treatment under optimized conditions (power, pressure, and time) identified via response surface methodology (RSM). Subsequently, UAE-NADES extraction was performed to maximize the results of antioxidant activity (DPPH) and total phenolic content (TPC). The integrated CP-UAE-NADES process enhanced TPC and DPPH compared to the untreated sample (non-CP). The optimum conditions were 11.5 min, 52 W, and 0.65 mbar. The predicted values of the Box-Behnken design for TPC and DPPH were compatible with the experimental Furthermore, microbial load reduction and color stability were analyzed to ensure chickpea root quality and functionality. The combined extraction methodology offers a sustainable and eco-friendly approach for the valorization of chickpea root as a source of bioactives, with potential applications in functional foods, nutraceuticals, and pharmaceuticals.
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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) | 0.000 | 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) | 0.000 | 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".