Using NIR and ATR-FTIR spectroscopy to rapidly detect compression wood in<i>Pinus radiata</i>
Bibliographic record
Abstract
Compression wood has undesirable properties for structural timber and for paper production. Traditional methods of detecting it are often time consuming and subjective. This study aimed to rapidly and impartially detect compression wood through the use of attenuated total reflectance Fourier transform infrared spectroscopy (ATR-FTIR) and diffuse reflectance near infrared (NIR) spectroscopy. Compression wood and non-compression wood samples were obtained from young Pinus radiata D. Don trees grown in New Zealand. Longitudinal dimensional changes were measured during drying or water saturation of the samples; lignin and galactose contents were determined using conventional analytical techniques. Chemical composition was here a more reliable discriminator between wood types than longitudinal dimensional changes. It was shown that partial least-squares regression (PLS-R) or discriminatory analysis (PLS-DA) could be used to build models on the training samples that could discriminate between wood types of the independently grown validation samples. Ultimately, both types of spectroscopies could be used to discriminate between compression wood and non-compression wood either through prediction or discriminatory analysis with equal success. Investigation into spectral differences between wood types, including sequential mixtures of wood types, showed that for the mid-IR region absorbance at a well-resolved lignin band could be used to discriminate compression wood from non-compression wood. For NIR, a similar investigation showed that absorbance values at four separate wavenumbers or the 6000–5600 cm−1region of the first derivative spectra were required for that discrimination. It is proposed that if there is a gradual change in the chemical composition of compression wood with its severity, then IR spectroscopy could feasibly be used to rapidly determine compression wood severity.
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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 | 0.000 | 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) | 0.001 | 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".