Rapid analysis of poplar lignin monomer composition by a streamlined thioacidolysis procedure and near‐infrared reflectance‐based prediction modeling
Bibliographic record
Abstract
Determination of the physico-chemical attributes of plant cell walls, such as lignin content and composition, is of paramount importance in germplasm screening and for evaluating the results of plant breeding and genetic engineering. There are escalating needs for analyses to be robust, reproducible, accurate, and efficient. We have recently modified an established protocol for discrimination of lignin monomers, thioacidolysis, with the goal of increasing sample throughput while maintaining accuracy and reducing equipment load and consumption of reagents. Numerous methodological changes related to volume scaling, selection of the processing vessel, and sample handling were addressed. The revised protocol permitted rapid processing of some 50 or more samples per person per day. A direct comparison between methods using hybrid poplar (Populus alba x tremula) wood samples, resulted in quantities of p-hydroxyphenyl (H), guaiacyl (G), and syringyl (S) lignin monomers that were equivalent to those derived from the original protocol. The revised methodology was then applied to quickly generate phenotypic trait data from 267 hybrid poplar trees (including wild type and eight C4H::F5H transgenic lines), for the development of a near-infrared-based model for predicting the proportion of lignin monomers across a broad phenotypic range of S:G. The resulting partial least squares regression model performed well under full cross-validation, giving strong, linear relationships between actual and predicted monomer proportions, and very high predictive accuracy for the predominant G and S monomers. This research brings considerable refinement to the thioacidolysis procedure, and establishes a method for rapidly and accurately quantifying cell-wall lignin composition that could effectively be employed in routine phenotypic screening platforms.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".