Determination of glyphosate residues in lentils using near-infrared hyperspectral imaging coupled with chemometric regression techniques
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The accurate measurement of pesticide content in lentils throughout the supply chain is essential to ensure compliance with the maximum residue level (MRL) regulations set by government agencies. The objective of this research was to study the feasibility of using near-infrared (NIR) hyperspectral imaging (HSI) system in the 900–2500 nm wavelength range to detect glyphosate residue levels in black beluga lentil, red lentil, large green lentil, and French green lentil at five glyphosate concentration levels (0 (control), 5, 10, 15 and 20 mg/kg). The prediction of the glyphosate content was achieved by developing partial least squares regression (PLSR) and principal component regression (PCR) models using different spectral preprocessing techniques on full spectrum and variables selected by selectivity ratio (sRatio) and variable importance in projection (VIP) methods. The full spectrum results showed that in black beluga lentil orthogonal spectral correction (OSC)-PLSR dataset performed best with correlation coefficient of prediction (R 2 p ), root mean square error of prediction (RMSEP) and residual predictive deviation (RPD) values of 0.916, 2.294 and 3.805, respectively. Further in red lentil the standard normal variate (SNV)-1 st derivative-PLSR performed best with a R 2 p , RMSEP and RPD values were 0.920, 2.190 and 3.925, respectively. Whereas in large green lentil and French green lentil, the 1st derivative-PLSR performed efficient with a R 2 p , RMSEP and RPD values were 0.938, 1.900 and 3.623 and 0.929, 2.017 and 3.413, respectively. Further, based on the wavelengths selected by VIP method, the OSC-VIP-PLSR model performed best for black beluga lentil with R 2 p , RMSEP and RPD values of 0.933, 1.915 and 3.595, respectively. In red lentil, the VIP-SNV + 1st derivative-PLSR showed highest performance with R 2 p , RMSEP and RPD values of 0.925, 2.066 and 3.332, respectively. Whereas, in large green lentil and French green lentil VIP-1st derivative-PLSR depicted highest prediction accuracy with R 2 p , RMSEP and RPD 0.940, 1.741 and 3.954 and 0.941, 1.726 and 3.988, respectively. This study demonstrated that the hyperspectral imaging system in 900–2500 nm range combined with machine learning could be used for the rapid and accurate glyphosate detection in lentils.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.006 |
| 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 it