High-throughput prediction of physical and mechanical properties of paper from Raman chemometric analysis of pulp fibres<sup>1</sup>This article is a contribution to the series The Role of Sensors in the New Forest Products Industry and Bioeconomy.
Bibliographic record
Abstract
Better pulp quality control, especially relating to product sheet strength, can offer an important means to improve the market superiority of a pulp. However, aside from lignin content, few pulp properties can be easily measured in a timely manner for process control. The present report proves a principle on a novel Raman system and a systematic chemometric approach, allowing for rapid spectral data acquisition and definitive spectrochemical analysis of wet pulp under harsh manufacturing conditions. By incorporating sophisticated chemometrics strategies that combine wavelet transform with a template-oriented genetic algorithm feature selection and partial least squares multivariate classification, this instrument system extracts maximum analytical information from raw Raman spectra to accurately predict the physicomechanical properties of sheet products. Our work has tested and refined these routines by drawing upon the systematic analysis of 26 bleached softwood kraft pulps formulated to yield challenges representative of those encountered in common pulp fibre and sheet analyses. Satisfactory calibration results suggest that a Raman gauge can be developed that has the capacity to continuously assay pulp to predict the physicomechanical properties of sheet paper in real time.
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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 | 0.001 | 0.001 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".