Use of Apple Pomace as Substrate for Production of Lactiplantibacillus plantarum Malolactic Starter Cultures
Bibliographic record
Abstract
The by-products of the food industry are an economic alternative as a source of nutrients to obtain biomass. At the same time, theiruse could solve the environmental problem related to their disposal, which is highly polluting due to their elevated biochemical oxygen demand. In this work, we seek to optimize the production of cellular biomass of two native Patagonian strains of Lactiplantibacillus plantarum (UNQLp 11 and UNQLp155), selected for its oenological and technological properties, using apple pomace (AP), a residue from the juice and cider industry. The supplementation of AP with yeast extract, salts, and Tween 80 (sAP), proved to maintain the growth of the Lpb. plantarum strains, similar to the commercial medium used to grow LAB (De Man, Rogosa, Sharpe, MRS). Cultures grown in sAP medium showed good tolerance to wine conditions (high ethanol content and low pH), demonstrated by its ability to consume L-malic acid. The subsequent inoculation of these cultures in sterile wines (Merlot and Pinot noir) was carried out at laboratory scale, evaluating cell viability and L-malic acid consumption for 21 days at 21 °C. Cultures grown in sAP media showed a similar performance to MRS media. Thus, sAP media proved to be a suitable substrate to grow oenological Lpb. plantarum strains where cultures (with high size inoculums) were able to drive malolactic fermentation, with an L-malic acid consumption higher than 90%.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".