Maple sap as a rich medium to grow probiotic lactobacilli and to produce lactic acid
Bibliographic record
Abstract
AIMS: To demonstrate the feasibility of growing lactobacilli and producing lactic acid using maple sap as a sugar source and to show the importance of oligosaccharides in the processes. METHODS AND RESULTS: Two maple sap samples (Cetta and Pinnacle) and purified sucrose were used as carbon sources in the preparation of three culture media. Compared with the sucrose-based medium, both maple sap-based media produced increased viable counts in two strains out of five by a factor of four to seven. Maple sap-based media also enhanced lactic acid production in three strains. Cetta sap was found to be more efficient than Pinnacle sap in stimulating lactic acid production and, was also found to be richer in various oligosaccharides. The amendment of the Pinnacle-based medium with trisaccharides significantly stimulated Lactobacillus acidophilus AC-10 to grow and produce lactic acid. CONCLUSIONS: Maple sap, particularly if rich in oligosaccharides, represents a good carbon source for the growth of lactobacilli and the production of lactic acid. SIGNIFICANCE AND IMPACT OF THE STUDY: This study provides a proof-of-concept, using maple sap as a substrate for lactic acid production and for the development of a nondairy probiotic drink.
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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) | 0.000 | 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".