Method for Rapid Determination of Hexeneuronic Acid in Non‐Wood Pulp by Multivariate Analysis of <scp>FT</scp>‐<scp>NIR</scp> Spectroscopic Data
Bibliographic record
Abstract
ABSTRACT In the chlorine dioxide bleaching stage of elemental chlorine‐free (ECF) chemical pulp production, hexeneuronic acid (HexA) in the brown stock contributes to the kappa number and consumes chlorine dioxide. This study aims to develop a feasible, environmentally friendly, and rapid method for quantifying HexA content in non‐wood pulp using FT‐NIR spectroscopy combined with chemometric modeling. The HexA levels were measured in 44 non‐wood pulp samples to validate the approach. The same samples were analyzed using FT‐NIR spectroscopy, and the obtained spectral data were preprocessed using Savitzky–Golay (S–G) smoothing followed by first and second derivatives, a common approach in chemometric analysis. Principal component regression (PCR) and partial least squares regression (PLSR) were evaluated for HexA quantification using both raw and pretreated FT‐NIR spectra. The PLSR model demonstrated superior predictive performance ( R 2 = 94.24%) when applied to FT‐NIR data preprocessed with Savitzky–Golay filtering, derivatives, and leverage correction. Finally, the validated model exhibited excellent accuracy in predicting HexA content in unknown non‐wood pulp samples, achieving a high correlation coefficient ( r = 0.993).
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.007 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 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".