Easy and Rapid Detection of Grain Iron Content in Fingermillet [Eleusine Coracana (L.) Gaertn] Germplasm
Bibliographic record
Abstract
In this study, a preliminary evaluation of grain iron content in fingermillet using Perls’ Prussian blue reagent, a stain for Fe 3+was established. Prussian blue solution of two per cent concentration was used in identifying grain iron content of genotypes based on the development of blue colour intensity. The rank correlation between measured grain Fe content and the colour intensity score was highly significant and positive (r = 0.62; P<0.01), indicating that higher the Fe content in the grain, more will be the intensity of blue colour developed. Pearls’ Prussian blue method could be effectively used as an initial method of screening and genotypes can be scored for high grain Fe content. The grain Fe content of the same genotypes was quantified using Atomic Absorption Spectrophotometer method. Wide variation was observed in fingermillet genotypes for grain Fe content. It ranged from 3.46 (TNEc 0921) to 8.72 (TNEc 0601) mg per 100g of grain. The accessions namely TNEc 0308, TNEc 0407, TNEc 0601, TNEc 0788 and TNEc 0910 were rich in grain Fe content coupled with high grain yield per plant. Therefore, these accessions could be employed in the genetic improvement of fingermillet through hybridization or selection. This simple staining procedure could be used to screen genotypes with high Fe content in large number of germplasm accessions.
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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 | 0.000 |
| 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".