Exploring the effects of genotype, environment and genotype by environment interactions on field pea (Pisum sativum L.) protein and amino acid contents using near-infrared reflectance spectroscopy
Bibliographic record
Abstract
Field pea is an essential crop in Canada, and it possesses high value from nutritional, functional, and economic aspects. As a member of the pulse family, the protein and amino acid contents are key nutritional parameters for field peas, which are susceptible to their genotypes and environmental conditions. Advanced methods are needed to overcome traditional wet chemistry's time and cost inefficiency. An optical technique, near-infrared reflectance spectroscopy (NIRS) provides a potential solution by being fast, chemical free and non-destructive. This study focused on the development and evaluation of calibration models in NIRS to predict moisture, crude protein and 18 amino acids in field pea using two scanning systems: DA7250 and FT9700. A total of 480 pea samples cultivated in Saskatchewan, Canada, were selected for comprehensive chemical analysis to form the calibration set. Calibration models were developed using partial least squares (PLS) regression equation. Overall, the NIRS calibration models exhibit an ideal performance in predicting protein and amino acids, excluding cysteine, methionine, and tryptophan. Since NIRS is an analytical method based on the reflection and absorption of light, the form of the sample and type of NIRS platform could influence the accuracy and precision of spectrum data. Calibration models based on DA7250 indicate that whole seeds are more suitable than ground seeds in NIR analysis. Regarding the performance difference between DA7250 and FT9700, significant differences were observed in analyzing protein and most amino acids, except Arg, Asp, Lys, Met, Thr, Trp and Tyr. Overall, DA7250 possessed a superior predictive capability over FT9700, which is advised in future applications. To clarify the potential impact of external factors on the quality of pea protein, more focus was put on the influence brought by the environment and genotype, as well as their interactions (G×E). NIRS models based on whole seeds from chapter one were used to predict crude protein, moisture, and amino acids in 8210 field peas from two breeding programs. A subsample of 2207 samples of 99 genetic lines from 2 harvesting years and 2 locations were carried out for G×E analysis. The observed results indicated that the genotype×environment interactions had significant influences on pea protein and amino acid contents. The year×location and year x genotype provided the majority of the variance in addition to the genotype's dominating influence. Additionally, protein content in peas was more sensitive to external factors than amino acid concentration.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".