A method for routine measurements of total sugar and starch content in woody plant tissues
Bibliographic record
Abstract
Several extraction and measurement methods currently employed in the determination of total sugar and starch contents in plant tissues were investigated with the view to streamline the process of total sugar and starch determination. Depending on the type and source of tissue, total sugar and starch contents estimated from samples extracted with 80% hot ethanol were significantly greater than from samples extracted with a methanol:chloroform:water solution. The residual ethanol did not interfere with the sugar and starch determination, rendering the removal of ethanol from samples unnecessary. The use of phenol-sulfuric acid with a phenol concentration of 2% provided a relatively simple and reliable colorimetric method to quantify the total soluble-sugar concentration. Performing parallel sugar assays with and without phenol was more useful for accounting for the interfering effects of other substances present in plant tissue than using chloroform. For starch determination, an enzyme mixture of 1000 U alpha-amylase and 5 U amyloglucosidase digested starch in plant tissue samples more rapidly and completely than previously recommended enzyme doses. Dilute sulfuric acid (0.005 N) was less suitable for starch digestion than enzymatic hydrolysis because the acid also broke down structural carbohydrates, resulting in overestimates of starch content. After the enzymatic digestion of starch, the glucose hydrolyzate obtained was measured with a peroxidase-glucose oxidase/o-dianisidine reagent; absorbance being read at 525 nm after the addition of sulfuric acid. With the help of this series of studies, we developed a refined and shortened method suitable for the rapid measurement of total sugar and starch contents in woody plant tissues.
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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 | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.006 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.006 | 0.007 |
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".