Next-generation sequencing approaches for improvement of lactic acid bacteria-fermented plant-based beverages
Bibliographic record
Abstract
Plant-based beverages and milk alternatives produced from cereals and legumes have grown in popularity in recent years due to a range of consumer concerns over dairy products. These plant-based products can often have undesirable physiochemical properties related to flavour, texture, and nutrient availability and/or deficiencies. Lactic acid bacteria (LAB) fermentation offers potential remediation for many of these issues, and allows consumers to retain their perception of the resultant products as natural and additive-free. Using next-generation sequencing (NGS) or omics approaches to characterize LAB isolates to find those that will improve properties of plant-based beverages is the most direct way to product improvement. Although NGS/omics approaches have been extensively used for selection of LAB for use in the dairy industry, a comparable effort has not occurred for selecting LAB for fermenting plant raw substrates, save those used in producing wine and certain types of beer. Here we review the few and recent applications of NGS/omics to profile and improve LAB fermentation of various plant-based substrates for beverage production. We also identify specific issues in the production of various LAB fermented plant-based beverages that such NGS/omics applications have the power to resolve.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".