Bioethanol Production From Pineapple Wastes
Bibliographic record
Abstract
<p>There is great interest in producing bioethanol from biomass and there is much emphasis on exploiting lignocellulose sources, from crop wastes through to energy-rich crops. Some waste streams, however, contain both cellulosic and non-cellulosic sugars. These include wastes from pineapple processing.</p> <p>Pineapple wastes are produced in large amounts throughout the world by canning industries. These wastes are rich in intracellular sugars and plant cell walls which are composed mainly of cellulose, pectic substances and hemicelluloses. The purpose of this study was to investigate the potential to transform such residues into ethanol after enzymatic saccharification of plant cell walls, and fermentation of the resulting simple sugars using the <em>Saccharomyces cerevisiae</em> NCYC 2826 strain. Three different fermentation modes, direct fermentation, separate hydrolysis and fermentation, and simultaneous saccharification and fermentation of the biomass were tested and compared. The results show that the main sugars obtained from pineapple waste were: glucose, uronic acid, xylose, galactose, arabinose and mannose. The highest ethanol yield was achieved after 30 hours of simultaneous saccharification and fermentation, and reached up to 3.9% (v/v), corresponding to the 96% of the theoretical yield.</p>
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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 | 0.001 | 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".