Effect of Oil Heat Treatment on Chemical Constituents of Semantan Bamboo (Gigantochloa scortechinii Gamble)
Bibliographic record
Abstract
Effect of oil heat treatment on chemical constituents of 3 years old Gigantochloa scortechinii Gamble bamboo was investigated. The bamboo splits within epidermis were heat-treated using crude palm oil at temperature 140°C, 180°C and 220°C for duration 30 and 60 min. After removed the epidermis, the samples were then grind to pass a BS 40-mesh sieve and retained on a BS 60-mesh sieve. The sawdust was air dried for several days before conducted to chemical analyses (cellulose, hemicellulose and lignin) based on TAPPI Standard Methods. The colorimetric method devised by Humprey and Kelly (1960) was adapted to analysis starch in bamboo. Reading was obtained through Baush Lomb UV Spectrophotometer at 650 mm calculated by standard reference using A.R. potato starch. Control was used as comparison for each type of test conducted. There was no significant different between control and condition at 140°C for 60 min (81.4%) of holocellulose content. The value was decreased by 2.1 to 10.7% (79.7 to 72.7%) after heating at 180 to 220°C for 30 to 60 min. The hemicellulose content of bamboo was ranged 24.1 to 27.8% after heating at 140-220°C for 30 to 60 min. The cellulose content of heat-treated bamboo was ranged 47.4 to 55.2% after reduced about 2 to 14%. Lignin content increased about 16% (26%) at 220°C/60 min after reduced approximately 1 to 5% at 140 to 180°C for 30 to 60 min. Starch content was largely reduced about 2 to 54% (4 to 1.9%) at 140 to 180°C for 30 to 60 min of treatment. The results indicated that degradation of cellulose and hemicellulose of heat-treated bamboo was attributed to plasticization of lignin during heating in the same time hydrolysed the starch content.
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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 | 0.001 |
| 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".