Determination of Main Constituents in Green Gram Using Near- Infrared Hyperspectral Imaging
Bibliographic record
Abstract
For the determination of main constituents, grain research laboratories around the world are using age old techniques which are time consuming, cost intensive, and sample destructive. In the present study, an attempt was made to investigate the feasibility of near-infrared (NIR) hyperspectral imaging for predicting moisture, protein, and starch content in green gram (Vigna radiata (L.) R. Wilczek). Images of green gram were obtained using a NIR hyperspectral imaging system in the wavelength region of 960-1700 nm at 10 nm intervals. Seventy five NIR reflectance intensities were extracted from each of the scanned images and were used in the development of prediction models. Ten-factor partial least squares regression (PLSR) and principal components regression (PCR) models were developed using a ten-fold cross validation for prediction. Prediction performances of PLSR and PCR models were assessed by calculating the estimated mean square errors of prediction (MSEP), standard error of cross-validation (SECV), and correlation coefficient (r). Overall, PLSR models demonstrated better prediction performances than the PCR models for predicting moisture, protein, and starch content of green gram. Based on β-coefficient values of the PLSR method, wavelengths regions of 1180-1220 and 1320-1360 nm; 960-980 and 1100-1110; and 1050-1100, 1230-1360, and 1400-1450 nm could be used in future inline inspection for predicting moisture content, protein, and starch content of green gram, respectively in multi-spectral imaging systems.
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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 | 0.000 |
| 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".