Prediction of protein and amino acid contents in whole and ground lentils using near-infrared reflectance spectroscopy
Bibliographic record
Abstract
Lentil (Lens culinaris Medik) is an important source of plant-based protein, and the protein and amino acid contents have a significant influence on its nutritional value and use. This study developed near-infrared reflectance spectroscopy (NIRS) calibration models to predict the protein and 18 amino acid contents of lentil seeds. The effects of sample status (whole and ground), type of spectrometer (DA 7250 and FT 9700), and amino acid/protein correlation on model performance were analyzed and evaluated. In total, 361 lentil samples grown in Saskatchewan, Canada, were selected as a calibration set. These samples were scanned by spectrometers and analyzed by reference wet chemistry methods to obtain spectral data and reference data, respectively. NIRS models developed by partial least squares (PLS) equation had a satisfactory performance for measuring protein and most amino acids (except for histidine, tyrosine, methionine, and cysteine) in lentils with high coefficients of determination for calibration (R2C = 0.652–0.927) and residual predictive deviation (RPD = 1.570 – 3.101). NIRS models from DA 7250 achieved similar accuracy for the determination of crude protein and amino acids in whole and ground lentils. DA 7250 models had a slightly better predictive ability with higher coefficients of determination for cross-validation (R2CV) and RPD values than FT 9700 models for all compositions except histidine. However, the predicted data of the two spectrometers did not differ significantly (p > 0.05) for every composition. For amino acids highly correlated to crude protein, NIRS generally predicted them with higher accuracy. Overall, NIRS combined with PLS regression yielded significant potential for rapid and simultaneous prediction of protein and most amino acid contents in lentils with satisfactory accuracy, and these models were usable for research purposes or sample screening.
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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 | 0.000 | 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".