Solid-state fermentation of coconut kernel-cake as substrate for the production of lipases by the coconut kernel-associated fungus Lasiodiplodia theobromae VBE-1
Bibliographic record
Abstract
Plant oil-extracted seed-cakes are good fermentation substrates for producing lipases that find application in transesterification of seed oils into biodiesel. This work describes the production of lipases by five lipolytic, oil-seed associated fungi ( Aspergillus niger, Chalaropsis thielavioides, Colletotrichum gloeosporioides, Lasiodiplodia theobromae , and Phoma glomerata ) by Solid-State Fermentation (SSF) on eight plant oil-seed cakes. The highest lipase activity was from the Coelomycete Lasiodiplodia theobromae VBE-1 grown on coconut kernel-cake, and was selected to optimize lipase production. The effects of supplementing coconut kernel-cake with mineral salts and coconut oil on lipase production by L. theobromae VBE-1 resulted in enhanced lipase activity. The effects of time of growth, moisture content, initial pH, temperature, as well as nutritional factors (carbon, nitrogen, vegetable oils, surfactants) when added to coconut kernel-cake, on lipase production were examined by a one-factor-at-a-time approach, and identified key variables for optimization by Response Surface Methodology (RSM). A 2 6 factorial central-composite experimental design with eight starting points and six replicates at the central point was used for lipase optimization. After validating the predicted levels of the factors, lipase production rose to 698 U/g Dry Substrate (DS) over un-optimized conditions (450 U/g DS).
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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 | 0.000 |
| 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) | 0.000 | 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 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".