Characterisation and biofuel production potential assessment of eight switchgrass cultivars grown in Türkiye: Insights from principal component analysis
Bibliographic record
Abstract
This study investigates the relationships between the chemical characterisation parameters of eight switchgrass cultivars grown in Türkiye and their practical yields of ethanol and methane, using Pearson correlation and principal component analysis (PCA). Acid-insoluble lignin, cellulose, and total Kjeldahl nitrogen (TKN) showed moderate positive correlations with methane production, while proteins, acid-soluble lignin, and lignin had minimal influence. BoMaster exhibited the highest glucose content (41.0 %) in the second harvest, while Trail Blazer and Kanlow also showed elevated glucose levels, indicating strong ethanol production potential. The Cave in Rock cultivar yielded the highest methane output, at approximately 250 mL CH 4 /g VS, whereas Kanlow demonstrated superior ethanol potential, estimated at 75 L ethanol/tonne switchgrass. Methane production positively correlated with acid-insoluble lignin and cellulose content, while reducing sugars, elemental carbon, and total sugars strongly correlated with ethanol production. Cellulose and hemicellulose, however, showed weak correlations, suggesting that pre-treatment may enhance sugar release. PCA revealed that the first four principal components accounted for 84.7 % of the overall variance in ethanol production, with reducing sugars, carbon, total sugars, hydrogen, and proteins positively associated with the first component. This study fills a gap in the limited literature on the relationship between switchgrass characterization and its potential for bioethanol and biogas production, providing insights to optimize switchgrass as a renewable energy source.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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".