Protein and Oil Contents Determination in Wheat Using Near-infrared (NIR) Hyperspectral Imaging
Bibliographic record
Abstract
Grain quality laboratories around world strive for an effective technique to determine protein and oil contents in wheat. Feasibility of near-infrared (NIR) hyperspectral imaging technique was assessed by developing prediction models to determine protein and oil contents of common wheat classes grown in western Canada. Wheat bulk samples were scanned in a wavelength region of 960 1700 nm at 10 nm intervals using a long wavelength indium gallium arsenide (InGaAs) NIR camera. Seventy five NIR absorbance intensities were extracted from the scanned images and used for developing prediction models for protein and oil contents of wheat using the partial least squares regression (PLSR) method. Twelve and thirteen partial least squares (PLS) factors were used to develop PLSR models to predict protein and oil contents, respectively. The developed models explained 89% of protein variation and 68% of oil content variation, respectively, in wheat. Correlation coefficients of 0.94 and 0.83 were obtained for predicting protein and oil contents, respectively, using PLSR models. These results establish that NIR hyperspectral imaging can be used as an effective method to determine protein and oil contents in wheat.
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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 | 0.000 | 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".