Effect of lactobacilli on yeast growth, viability and batch and semi-continuous alcoholic fermentation of corn mash
Bibliographic record
Abstract
AIMS: The aim of this study was to evaluate interactions between Saccharomyces cerevisiae and selected strains of lactobacilli regarding cell viabilities, and production of organic acids and ethanol during fermentation. METHODS AND RESULTS: Corn mashes were inoculated with yeasts and selected strains of lactobacilli, and fermented in batch or semi-continuous (cascade) mode. Ethanolic fermentation rates and viabilities of yeast were not affected by lactobacilli unless the mash was pre-cultured with lactobacilli. Then, yeast growth was inhibited and the production of ethanol was reduced by as much as 22%. CONCLUSION: Yeasts inhibited the multiplication of lactobacilli and this resulted in reduced production of acetic and lactic acids. The self-regulating nature of the cascade system allowed the yeast to recover, even when the lactobacilli had a head start, and reduced the size of the population of the contaminating Lactobacillus to a level which had an insignificant effect on fermentation rate or ethanol yield. SIGNIFICANCE AND IMPACT OF THE STUDY: Contamination during fermentation is normally taken care of by the large yeast inoculum, although yeast growth and fermentation rates could be adversely affected by the presence of high numbers of lactobacilli in incoming mash or in transfer lines.
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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 | 0.001 |
| 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) | 0.001 | 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".