Determination of main constituents in wheat using near infrared hyperspectral imaging
Bibliographic record
Abstract
Differentiation of wheat classes and rapid measurement of main constituents (e.g., protein, starch, oil content, and moisture content) in wheat are important challenges to the grain industry.In this study, NIR reflectance and absorbance values of hyperspectral images of wheat samples were used for identifying the Canadian wheat classes at same and different moisture levels and for predicting protein and oil content of wheat.Images of wheat were obtained using a NIR hyperspectral imaging system.Seventy five normalized NIR mean reflectance and NIR absorbance features were extracted from the scanned images of wheat.The extracted features were used to develop classification models and prediction models for identifuing wheat classes; and predicting protein and oil contents of wheat, respectively.Classification accuracies were 100% in classifuing Canada Prairie Spring Red (CPSR), Canada Western Extra Strong (CWES), Canada Western Hard White Spring (CWHV/S), Canada Western Red Spring (CWRS), Canada Western Red winter (CV/RW), and Canada V/estern Soft White Spring (CWSV/S) wheat; and > 98% for rhe other two wheat classes (Canada Prairie Spring White (CPSW) and Canada Western Amber Durum (CWAD)) using linear discriminant analysis (LDA) with leave-one-out cross validation.Using quadratic discriminant analysis (QDA) with leave-one-out cross validation, the classification accuracies were classification accuracies of 80 -100% and 89 -100% were found for artificial neural network (AI.IN) models with two different training patterns such as 60%o training -30% test -10% validation (60-30-i0) and 70o/o training-20o/o test -I0o/o validation (70-20- 10), respectively.
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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".