Antioxidant Activity of the Lignins Derived from Fluidized-Bed Fast Pyrolysis
Bibliographic record
Abstract
A challenge in recent years has been the rational use of forest and agriculture residues for the production of bio-fuel, biochemical, and other bioproducts. In this study, potentially useful compounds from pyrolytic lignins were identified by HPLC-MS/MS and untargeted metabolomics. The metabolites identified were 2-(4-allyl-2-methoxyphenoxy)-1-(4-hydroxy-3-methoxyphenyl)-1-propanol, benzyl benzoate, fisetinidol, phenyllactic acid, 2-phenylpropionic acid, 6,3′-dimethoxyflavone, and vanillin. The 2,2-diphenyl-1-picrylhydrazyl radical scavenging activity (DPPH), trolox equivalent antioxidant capacity (TEAC), and total phenolics content (TPC) per gram of pyrolytic lignin ranged from 14 to 503 mg ascorbic acid equivalents, 35 to 277 mg trolox equivalents, and 0.42 to 50 mg gallic acid equivalents, respectively. A very significant correlation was observed between the DPPH and TPC (r = 0.8663, p ≤ 0.0001), TEAC and TPC (r = 0.8044, p ≤ 0.0001), and DPPH and TEAC (r = 0.8851, p ≤ 0.0001). The polyphenolic compounds in the pyrolytic lignins which are responsible for radical scavenging activity and antioxidant properties can be readily profiled with HPLC-MS/MS combined with untargeted metabolomics. The results also suggest that DPPH, TEAC, and TPC assays are suitable methods for the measurement of antioxidant activity in a variety of pyrolytic lignins. These data show that the pyrolytic lignins can be considered as promising sources of natural antioxidants and value-added chemicals.
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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".