Advancing the science of wheat quality evaluation using nuclear magnetic resonance (NMR) and ultrasound‐based techniques
Bibliographic record
Abstract
Background and objectives The need for cost‐effective, fast, and reliable techniques to evaluate wheat protein content and quality has led to the modification of available methods as well as the development of new methods. This review provides information on two emerging methods, that is, low‐field nuclear magnetic resonance (LF‐NMR) and low‐intensity ultrasound, with potential for evaluating wheat protein content and quality. Findings New techniques with additional capabilities have been developed over the years to provide wheat researchers with information that could not be acquired by the use of conventional methods. LF‐NMR and low‐intensity ultrasound are two emerging techniques in the world of cereal science whose speed, ease of operation, and reliability could promote their applications to wheat screening research studies. This review of these two potential wheat protein quality evaluation methods highlights the necessity of developing such cutting‐edge tools which are advantageous over the conventional tools. Conclusions Easy‐to‐use LF‐NMR and low‐intensity ultrasound seem to have the potential to provide nondestructive, fast evaluation methods that are superior to the currently used techniques in terms of accuracy and cost, respectively. However, further investigations to achieve complete development of such methods are still required. Significance and novelty The potential of LF‐NMR and low‐intensity ultrasound as fast, accurate, and cost‐effective wheat screening tools was confirmed through critical discussions and comparisons of a variety of most widely used wheat quality evaluation techniques.
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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 | 0.001 | 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 | 0.001 |
| 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".