Quantification and Variability Analysis of Lignin Optical Properties for Colour-Dependent Industrial Applications
Bibliographic record
Abstract
Lignin availability has increased significantly due to the commercialization of several processes for recovery and further development of alternatives for integration into Kraft pulp mills. Also, progress in lignin characterization, understanding of its chemistry as well as processing methods have resulted in the identification of novel lignin-based products and potential derivatives, which can serve as building block chemicals. However, all these have not led to the successful commercialization of lignin-based chemicals and materials. This is because most analyses and characterizations focus only on the technical suitability and quantify only the composition, functional groups present, size and morphology. Optical properties, such as the colour, which influences the uptake by users for diverse applications, are neither taken into consideration nor analysed. This paper investigates the quantification of lignin optical properties and how they can be influenced by process operating conditions. Lignin extraction conditions were also successfully correlated to the powder colour. About 120 lignin samples were collected and the variability of their colours quantified with the CIE L*a*b* colour space. In addition, a robust and reproducible colour measurement method was developed. This work lays the foundation for identifying chromophore molecules in lignin, as a step towards correlating the colour to the functional groups and the purity.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".