Pineapple peel waste enhances manure protein degradation: Statistical optimization
Bibliographic record
Abstract
Animal farms generate large amounts of manure suitable as feedstock for producing biogas by anaerobic digestion (AD). However, AD encounters difficulties when manure contains excessive protein levels. This study investigates using pineapple peel waste (PPW)-derived protease enzyme (bromelain) to enhance manure’s protein degradation and improve biogas production. It aims to improve the degradation of manure protein and mitigate the inhibitory ammonia accumulation problem. The study applied a Box–Behnken design and analyzed the data using the Response Surface Methodology (RSM) to optimize protein reduction and diminishing ammonia levels. It examined the single and two-way impacts of parameters such as manure dosage, PPW dosage, and degradation duration. The statistically derived optimal degradation condition for 36±0.25% protein reduction was observed at 9 g VS manure L -1 , and 4 g VS PPW L -1 at 48 h degradation. However, the highest reduction of ammonia nitrogen (NH 3 -N) by 72±0.48% was achieved under the optimal combinations of 6.5 g VS manure L -1 , and 7 g VS PPW L -1 at 48 h degradation. Fourier Transform Infrared (FTIR) spectroscopy and Scanning Electron Microscopy (SEM) analyses revealed changes, particularly weakening and cleavage of hydrogen and amide I, II, and III bonds, confirming hydrolyzed manure's protein structural and morphological alterations. The hydrolyzed substrate characterization, paired with the rigorously developed statistical data, strongly supports using PPW as an effective agent to address the ammonia accumulation challenges. PPW significantly and effectively enhances protein breakdown within manure, potentially increasing hydrogen and methane generation during AD.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".