Multivariate Analysis of Hemicelluloses in Bleached Kraft Pulp Using Infrared Spectroscopy
Bibliographic record
Abstract
The hemicellulose composition of a pulp significantly affects its chemical and physical properties and thus represents an important process control variable. However, complicated steps of sample preparation make standard methods for the carbohydrate analysis of pulp samples, such as high performance liquid chromatography (HPLC), expensive and time-consuming. In contrast, pulp analysis by attenuated total internal reflection Fourier transform infrared spectroscopy (ATR FT-IR) requires little sample preparation. Here we show that ATR FT-IR with discrete wavelet transform (DWT) and standard normal variate (SNV) spectral preprocessing offers a convenient means for the qualitative and quantitative analysis of hemicelluloses in bleached kraft pulp and alkaline treated kraft pulp. The pulp samples investigated include bleached softwood kraft pulps, bleached hardwood kraft pulps, and their mixtures, as obtained from Canadian industry mills or blended in a lab, and bleached kraft pulp samples treated with 0-6% NaOH solutions. In the principal component analysis (PCA) of these spectra, we find the potential both to differentiate all pulps on the basis of hemicellulose compositions and to distinguish bleached hardwood pulps by species. Partial least squares (PLS) multivariate analysis gives a 0.442 wt% root mean square errors of prediction (RMSEP) for the prediction of xylan content and 0.233 wt% RMSEP for the prediction of mannan content. These data all support the idea that ATR FT-IR has a great potential to rapidly and accurately predict the content of xylan and mannan for bleached kraft pulps (softwood, hardwood, and their mixtures) in industry. However, the prediction of xylan and mannan concentrations presented a difficulty for pulp samples with modified cellulose crystalline structure.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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".