Conversion of Phenolic Acids in Canola Fermentation: Impact on Antimicrobial Activity against <i>Salmonella enterica</i> and <i>Campylobacter jejuni</i>
Bibliographic record
Abstract
Canola meal (CM) is commonly used in poultry feeds. CM has a high protein content but also contains high levels of antimicrobial phenolic acids. Lactic acid bacteria can alter CM phenolic composition during fermentation and influence its antimicrobial activity against pathogens. Fermented CM was analyzed for phenolic composition using tandem mass spectrometry (LC–MS/MS) and high-performance liquid chromatography (HPLC). Sinapic acid and derivatives were the major phenolic acids in CM. Growth of lactobacilli in CM was attenuated when compared to cereal substrates. Glucosides and esters of sinapic acid were extensively hydrolyzed during fermentation with Lactiplantibacillus plantarum and Furfurilactobacillus milii . Lp. plantarum transformed hydroxycinnamic acids to dihydro, 4-vinyl, and 4-ethyl derivatives, Ff. milii reduced hydroxycinnamic acids to dihydroderivatives, but Limosilactobacillus reuteri did not convert hydroxycinnamic acids. The minimum inhibitory concentration of phenolic extracts was assessed with lactobacilli, Salmonella, and Campylobacter jejuni as indicator strains. Fermentation of CM with Lp. plantarum or Ff. milii increased the antimicrobial activity of phenolic extracts against Salmonella enterica and Campylobacter jejuni . Fermentation with Lm. reuteri TMW1.656 but not fermentation with Lm. reuteri TMW1. 656 Δ rtcN increased the antimicrobial activity of extracts owing to the production of reutericyclin. This study demonstrates that fermentation of CM with lactobacilli converts hydroxycinammic esters and may increase the antimicrobial activity of phenolic compounds in CM against pathogens.
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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 | 0.001 |
| 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 | 0.000 |
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".