Effect of Forest Biomass Pretreatment on Essential Oil Yield and Properties
Bibliographic record
Abstract
Essential oils (EOs) are natural and economically valuable aromatic compounds obtained from a variety of crops and trees, including forest trees, which have different therapeutic and biological activities. This project aims to assess the impact of different residual forest biomass pretreatments on the yield and the properties of EOs, including their antibacterial and antioxidant characteristics. Forest biomass from black spruce (BS, Picea mariana Mill.), balsam fir (BF, Abies balsamea), and jack pine (JP, Pinus banksiana Lamb.) was processed mechanically by (i) shredding, (ii) grinding, (iii) pelletizing, and (iv) bundling. EOs were then extracted by hydro- and steam distillation. The densification into bundles was found to improve EOs yield compared to the other residual forest biomass pretreatments. For example, the yield of bundled BF was improved by 68%, 83%, and 93% compared to shredded, ground, and granulated biomass, respectively. The highest yield was obtained when densification into bundles was combined with extraction through hydrodistillation. As for EOs’ chemical composition, JP had the highest polyphenol content and consequently the greatest antioxidant activity. EOs derived from BS inhibited the growth of Gram-positive Staphylococcus aureus bacteria and Gram-negative Salmonella typhimurium and Escherichia coli bacteria. The densification of forest biomass into bundles did not affect the antioxidant capacity or the antibacterial activity of EOs, thereby preserving both properties. Thus, the pretreatment of forest biomass residue could have an impact on the volume and the transport costs and therefore improve the bioproducts market and the bioeconomy in Canada.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 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) | 0.000 | 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 teacher head, 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".