Fermentation of Barley by Using <i>Saccharomyces cerevisiae</i> : Examination of Barley as a Feedstock for Bioethanol Production and Value-Added Products
Bibliographic record
Abstract
The objective of this study was to examine the ethanol yield potential of three barley varieties (Xena, Bold, and Fibar) in comparison to two benchmarks, corn and wheat. Very high gravity (VHG; 30% solids) fermentations using both conventional and Stargen 001 enzymes for starch hydrolysis were carried out as simultaneous saccharification and fermentation. The grains and their corresponding dried distiller's grain with solubles (DDGS) were also analyzed for nutritional and value-added characteristics. A VHG traditional fermentation approach utilizing jet-cooking fermentation revealed that both dehulled Bold and Xena barley produced ethanol concentrations higher than that produced by wheat (12.3, 12.2, and 11.9%, respectively) but lower than that produced by corn (13.8%). VHG-modified Stargen-based fermentation of dehulled Bold barley demonstrated comparable performance (14.3% ethanol) relative to that of corn (14.5%) and wheat (13.3%). Several important components were found to survive fermentation and were concentrated in DDGS. The highest yield of phenolics was detected in the DDGS (modified Stargen 001, 20% solids) of Xena (14.6 mg of gallic acid/g) and Bold (15.0 mg of gallic acid/g) when the hull was not removed before fermentation. The highest concentration of sterols in DDGS from barley was found in Xena (3.9 mg/g) when the hull was included. The DDGS recovered from corn had the highest concentration of fatty acids (72.6 and 77.5 mg/g). The DDGS recovered from VHG jet-cooking fermentations of Fibar, dehulled Bold, and corn demonstrated similar levels of tocopherols and tocotrienols. Corn DDGS was highest in crude fat but was lowest in crude protein and in vitro energy digestibility. Wheat DDGS was highest in crude protein content, similar to previous studies. The barley DDGS was the highest in in vitro energy digestibility.
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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 | 0.000 |
| 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".