Chemical Composition of Black Spruce ( <i>Picea mariana</i> ) Bark Extracts and Their Potential as Natural Disinfectant
Bibliographic record
Abstract
The valorization of residual forest biomass from sawmills is an economic and ecological opportunity in Québec. With specialized metabolites and biological activities, several residues from Québec's tree species could have commercial potential. This study aims to study the antimicrobial efficacy of extracts from bark residues to determine their potential as a natural disinfectant. We first performed a quantification of phenolic metabolites by colorimetric tests which showed higher flavonoids and proanthocyanidins content (>27.88 mmol catechin equivalents (CE)/100 g of bark extract and >3.90 mmol CE/100g of bark extract respectively) in black spruce extracts compared to balsam fir, quaking aspen and white birch. Extraction with water (WE) followed by fractionation with ethyl acetate yielded a fraction enriched with oligomeric proanthocyanidins (OPF). WE and OPF antimicrobial activity on Escherichia coli using the broth microdilution and the dilution-neutralization methods (AOAC 960.09) demonstrated an increased antimicrobial potency with OPF. A minimal inhibitory concentration and a minimum bactericidal concentration of 0.83 mg/mL and 4.44 mg/mL respectively as well as a microbial reduction of 4.83 log CFU/mL (3% w/w with 10 min contact) and ≥5.09 log CFU/mL (1.5% w/w with 120 min contact time) were obtained. Compounds characterization using UPLC-QTOF-MS allowed to putatively identify nine antimicrobial compounds in the OPF. Taxifolin, dihydroxykampferol and andrographolide seemed to be associated with the increase of the antimicrobial activity of this fraction.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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".