Effect of Pre-treatments in the Processing of Pineapple Leaf Fibers
Bibliographic record
Abstract
Hot water and alkali treatments were performed on pineapple leaves at three different temperatures (50ºC, 70ºC and 90ºC) and three different levels of concentrations of NaOH solution (2, 4, and 6%).The effect of pre-treatment was analyzed by comparing the physical qualities of pineapple leaf fiber (PALF).A physical property analysis of the PALF extracted after two pretreatments was carried out by measuring tensile strength, percentage of elongation, color, and surface properties of PALF.The PALF pre-treated at 70°C for 15 min showed maximum average tensile strength of 1206.3±753.02MPa and the lowest value of 353.1± 41.51 MPa were recorded for PALF pre-treated at 90 ºC for 45 min.In alkali pre-treated PALF, the maximum tensile strength of 1137.2 ± 28.01 MPa was recorded for pineapple leaves treated with 2% NaOH for 6h.In both hot water and alkali treated PALF, the percentage elongation was lower compared to the non-treated PALF.Hot water treated PALF recorded higher percentage of elongation than Alkali treated PALF.The tensile strength and percentage elongation in both treatments showed similar increase with respect to an increase in temperature and alkalinity.PALF treated with hot water showed more color change than Alkali treated PALF.The hot water treated PALF registered a maximum color change (ΔE) of 23.692 (70 ºC -45 min) while the alkali treated PALF showed a maximum color change (ΔE) of 19.721 (6%-2h).However, the latter showed better surface properties than hot water treated PALF.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".