FT-IR and HPLC analysis of silver fir (Abies alba Mill.) bark compounds from different geographical provenances
Bibliographic record
Abstract
Fourier Transform Infrared Spectroscopy (FT-IR) and High-Performance Liquid Chromatography (HPLC) could be applied to study the provenance of wood, specifically the differentiation of wood resources, as well as the identification of chemical compounds that are connected to the changes that occur in wood as a result of drying treatments. To test this hypothesis, the bark of silver fir ( Abies alba Mill.) from trees belonging to seven different geographical provenances was studied, using samples dried at three different temperatures (60, 80, and 100 °C). FT-IR spectroscopy revealed different band assignments in the mid-infrared region depending on fir provenances, whereas the vibrational bands of the biomass functional groups tended to shift to lower wavenumbers. Significant differences were identified between the chemical compounds in the bark depending on the provenances. The largest proportion of the total phenolics was represented by the epicatechin gallate, epicatechin, catechin, and procyanidin dimer B1. Exploratory data analysis was performed using principal component analysis (PCA), hierarchical clustering, and Pearson correlations. This allowed a comparative evaluation of the samples and interpret the findings according to the geographical provenances, respectively ecological conditions in the areas of origin, but also the influence of the drying temperatures of the samples on chemical compounds. The precipitation in the areas of origin decreased total phenolics in silver fir bark samples, and total phenolics differed not only due to the geographic provenance, but also due to drying temperature.
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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) | 0.000 | 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) | 0.000 | 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".