Evaluation of X‐Ray Fluorescence Spectroscopy as a Tool for Nutrient Analysis of Pea Seeds
Bibliographic record
Abstract
ABSTRACT This research was conducted to evaluate the utility and reliability of X‐ray fluorescence (XRF) spectroscopy to analyze macro‐ (K and Ca) and micronutrients (Mn, Fe, Cu, Zn, and Se) in pea (Pisum sativum L.) seeds. The pea seed samples were ground into flour and pelleted to collect the XRF spectra. Seventy‐three pea seed samples were selected to cover the expected concentration ranges for each element to develop calibration curves by correlating the XRF results with atomic absorption spectroscopy (AAS). The XRF results were validated by a systematic comparison of data obtained from AAS on a set of 80 additional and independent pea seed samples. Element concentrations were also predicted using the fundamental parameter approach collectively for 153 samples. For all the calibration curves, the R2 value was >0.8, except for K (0.54). For Mn, Fe, Cu, Zn, and Se, the XRF predictions were similar to AAS measurements at a 95% confidence level. Similar results were obtained with the fundamental parameter approach except for Fe for which significant bias of ∼6 mg kg−1 was calculated. Except for K, R value for all the validation curves was >0.85. Thus, the results obtained using XRF and the fundamental parameter approach were statistically not different from the AAS method. This study demonstrated that the XRF technique is a fast and reliable, nondestructive, and noninvasive analytical tool for mineral analysis, particularly for transition metals, does not produce waste, and requires no chemical reagents.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".