OPTIMIZATION OF GLUTEN PEAK TESTER: A STATISTICAL APPROACH
Bibliographic record
Abstract
ABSTRACT Response surface methodology was applied to develop a standard method for gluten peak tester. Four variables – flour weight, temperature, solvent and rpm – were varied as per the center composite design, and the responses – torque and peak maximum time – were analyzed. Flour–solvent interaction was observed to be the most significant factor impacting the peak torque for whole meal and hard wheat flours while flour (g) and rpm were the most significant for soft wheat flour and insignificant for whole meal flour. The setting 8.5 g flour, 9.5 g solvent (0.5 M CaCl2), 34C temperature and 1,900 rpm was obtained as the standard setting applicable to whole meal as well as refined flours from soft and hard wheats. PRACTICAL APPLICATIONS Gluten quality is an important criterion to predict flour performance in cereal processing industry. The gluten peak tester has been recently introduced as a sensitive and rapid way of testing wheat gluten quality. The current research was designed to optimize the gluten peak tester to work with wheat varieties with a wide range of protein contents so as to lay a baseline for method development to assess gluten quality of different wheat varieties/lines within a short time span with minimum sample requirements which is very critical for the breeding industry.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.021 | 0.026 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".