Assessment of Nine Micronutrients in Jasmine 85 Rice Grown in Ghana Using Neutron Activation Analysis
Bibliographic record
Abstract
The amount of micronutrients in food is a key factor that determines the health status of a person. The concentrations of nine micronutrients, Sodium (Na), Magnesium (Mg), Chlorine (Cl), Potassium (K), Calcium (Ca), Vanadium (V), Manganese (Mn), Copper (Cu) and Iodine (I), in polished Jasmine 85 rice, locally cultivated in five rice farming areas in Ghana (Afienya, Afife, Dawhenya, Ashaiman and Aveyime), were determined using Neutron Activation Analysis. The standard materials used as reference were the International Atomic Energy Agency (IAEA)-530 Tuna fish homogenate and the National Institute of Standard and Technology (NIST) USA 1566b Oyster Tissue. Recoveries of the elemental concentrations ranged from 88% to 111% of the certified values. Relative standardization method was used in the quantification of the elements. The range of concentrations measured in the rice are: 142.3-188.1 mg/kg for Na, 483.2-875.7 mg/kg for Mg, 465.6-718.0 mg/kg for Cl, 514.6-2949.0 mg/kg for K, 2303.0-2622.0 mg/kg for Ca, 0.0698-0.1925 mg/kg for V, 9.956-14.460 mg/kg for Mn, 0.8728-1.6790 mg/kg for Cu and 0.1181-0.1447 mg/kg for I. Using Hierarchical clustering analysis and Principal Component Analysis to evaluate the intensities of measured concentrations, K was established to be the most abundant, and was used to categorize two distinct clusters; Group 1 farms (Ashaiman, Afienya, and Dawhenya) and Group 2 farms (Aveyime and Afife). Group 2 farms recorded elevated intensities of micronutrients. With Pearson's correlation coefficient, some noteworthy correlations realized were between Na and K (r = 0.951), Na and V (r = 0.842) and K and V (r = 0.812). This indicated the same or similar source inputs for each pair. The calculated mean daily intake of K exceeded the mean Recommended Dietary Allowable and Adequate Intake for all Life Stage Groups. Estimated health risk associated with the consumption of rice was only present for children between the ages of 1 and 3 for Mg. The information on these nine micronutrients content of the rice from these five farming areas would be valuable in rice consumption studies to evaluate the overall availability of micronutrients to the Ghanaian populace and age groups and also in nutrition planning for analysis of nationwide rice supplies, mainly for regions and countries known to be susceptible to deficiencies of these micronutrients. The techniques espoused in this research can be used to accurately determine the concentration of micronutrients in rice and also trace the area where the rice was produced.
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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".