Ultrasound‐assisted extraction and purification of bromelain from pineapple ( <scp> <i>Ananas comosu</i> </scp> <i>s</i> ) stem waste using ethanol precipitation and resin adsorption
Bibliographic record
Abstract
Abstract Bromelain, a proteolytic enzyme from the Bromeliaceae family, is valued for its industrial applications and high demand, particularly in pharmaceuticals. This study evaluated ultrasound‐assisted extraction and purification of bromelain from pineapple ( Ananas comosus (L.) Merril) stem waste for industrial‐scale potential, involving raw material characterization, ultrasound extraction with water, ethanol precipitation, and resin adsorption. The stem showed the highest enzymatic activity (2.255 ± 0.089 U · mL −1 without ultrasound, up to 3.622 U · mL −1 with ultrasound at optimal conditions: 1:2 tissue‐to‐water ratio, 25°C, 22% power), making it the primary raw material. Ultrasound enhanced activity by ~60% and reduced processing time to 10 min compared to conventional methods. Two‐stage ethanol precipitation (30% and 70% v/v) achieved 82% recovery, increasing specific activity from 0.852 to 1.143 U · mg −1 . Batch adsorption with Amberlite IRA 410 resin yielded a purification factor of 10.73 and specific activity of 9.143 U · mg −1 by removing non‐target proteins, though it did not selectively bind bromelain. Overall, this process confirms viability for industrial implementation in valorizing pineapple waste.
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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".