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.Classification accuracies of 100% were achieved using LDA with leave-one-out cross validation for CWSWS wheat at 14, 16, 18, and 2Oo/o moisture levels with 75 features.And, classification accuracies of > 90%o were achieved for all wheat classes except CWES wheat at20o/o moisture level and CWHV/S wheat at 14%;o moisture level in LDA with leave-one-out cross validation using 75 features.Plots of the first two canonical variables showed that protein and moisture contents of wheat could be predicted using the NIR absorbance values of hyperspectral images.principal components analysis (PCA) and STEPDISC procedure were used to find the top wavelengths in wheat class identification.A 75 feature PLSR model for predicting protein in wheat produced the best standard error of prediction (SEP : 0.68) and a good correlation (r : 0.94) with the measured protein in wheat.Also, the 75 feature PLSR model for predicting oil content in wheat produced the best SEP of 0.10 and a r value of 0.83 with the measured oil content in wheat.Results of this study showed that NIR hyperspectral imaging could be used as an effective method for predicting protein and oil contents in wheat and identifying wheat classes at different moisture levels.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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 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".