Development of a recombinant brewing yeast to produce beer from hemp extract (<em>Cannabis Sativa L.</em>)
Bibliographic record
Abstract
The Cannabis industry is a rapidly-growing market in Canada. With the legalization of edible products in 2019, many cannabis-derived candies, baked goods, beverages appeared on shelves. Cannabis beer can be brewed by replacing barley with pretreated cannabis plant. However, using a traditional brewing yeast to brew cannabis beer will result in incomplete fermentation which will affect the beer’s composition and flavour because traditional brewing yeasts are not able to utilize xylose which is an abundant carbohydrate in lignocellulosic extracts. Using a recombinant strain of a brewing yeast and a xylose-fermenting yeast can overcome this issue. The work presented in this thesis compares the fermentation performance of two native xylose-fermenting yeast strains, Pichia stipitis and Spathaspora passalidarum, and performs the transformation with a brewing yeast via electroporation. Fermentation performance of the xylose-fermenting yeasts were evaluated in mixed carbohydrate medium, containing cellobiose, glucose and xylose. Under aerobic conditions, carbohydrate consumption rates of both strains were faster than the rates under anaerobic conditions, but aerobic conditions led to ethanol respiration by P. stipitis and S. passalidarum. Under anaerobic conditions and at high glucose concentrations, S. passalidarum sequentially utilized glucose and xylose, while glucose decreased xylose utilization ability of P. stipitis. S. passalidarum also exhibited higher ethanol tolerance compared to P. stipitis. Transformation of brewing yeast strains and S. passalidarum were conducted using electroporation-based transformation. Genomic DNA of the donor strain, S. passalidarum, was extracted using phenol-chloroform extraction and transferred into host strains, an ale and a lager strain, using an electric pulse. Putative recombinants were selected on plates containing xylose as the sole carbon source, however, obtained recombinants strains were deemed to be unstable due to the aneuploid nature of the host strains.
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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 | 0.001 |
| 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".