Application of chemometrics to prediction of some wheat quality factors by near‐infrared spectroscopy
Bibliographic record
Abstract
Abstract Background and objectives Physical quality parameters of wheat include kernel texture and test weight, which affect classification, grading, and price. Physicochemical factors are associated with flour functionality. The objective of the work was to determine the effectiveness with which these factors could be predicted in early generations using near‐infrared spectroscopy (NIRS). Wheat breeders need to know how their new genetic lines carry these factors, which carry no identifiable absorbers in the NIR region, can be predicted in early generations, by experts in the use of NIRS and its associated chemometrics. Findings With the exception of protein content, for which absorbers are plentiful, and was included to verify the spectral quality of the sample sets, none of the strictly physical factors could be reliably predicted with the most widely used chemometric options in the hands of experts in their use. Random forest algorithms were capable of prediction of all physicochemical factors, except Farinograph development time, with a reasonable degree of reliability. Conclusions Reliable predictions of quality factors in wheat that can be predicted by NIRS with satisfactory reliability are limited to chemical and physicochemical factors for which absorbers exist in the NIR region. Significance and novelty Chemical, physicochemical, and physical factors can be predicted with acceptable reliability by NIRS using a computerized spectrophotometer in association with Random Forest software. Farinograph development time remains a challenge for NIRS application.
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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 | 0.005 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".