Separation and Characterization of Barley Starch Polymers by a Flow Field‐Flow Fractionation Technique in Combination with Multiangle Light Scattering and Differential Refractive Index Detection
Bibliographic record
Abstract
ABSTRACT Flow field‐flow fractionation (flow FFF) with frit inlet and frit outlet mode (FIFO) was coupled online to multiangle light scattering (MALS) and refractive index (RI) detectors to investigate the molecular characteristics of normal and zero amylose barley starch polymers. Application of two different cross‐flows, 0.35 mL/min followed by 0.1 mL/min, and constant channel and frit flows of 0.1 and 1.0 mL/min, respectively, permitted a complete separation of amylose and amylopectin. The improved signals from the detectors due to application of the FIFO mode enabled the proper characterization of the small molecular weight species, as well as significantly enhanced the reproducibility of the measurements. The weight‐average molecular weight (Mw) and zaverage root‐mean‐square (RMS) radii of gyration (Rg) values for amylose and amylopectin in the normal starch samples were 2.3 × 106 and 280 × 106, and 107 and 260 nm, respectively. The Mw and Rg of amylopectin in the zero amylose starch samples were 360 × 106 and 267 nm, respectively. The slopes (α) obtained by plotting log Mw versus log Rg for amylose and amylopectin were 0.6 and 0.3, respectively. These results are in good agreement with the theoretical prediction of the molecular conformation of amylose and amylopectin.
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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 | 0.001 | 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 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".