Evaluation of the Mineral Composition of Chia (Salvia Hispanica L.) Seeds from Selected Areas in Kenya
Bibliographic record
Abstract
Chia (Salvia hispanica L.) seeds are gaining popularity among consumers and food processors, particularly in food fortification. Consequently, there has been an increased need to determine the mineral composition of chia seeds cultivated in different regions to ascertain their potential in various food applications. In this study, 20 chia seeds samples obtained from farmers practicing commercial farming of chia seeds in selected areas in Kenya during the two main chia seed planting seasons (April-August 2019) and (September-December 2019) were analyzed for their mineral content using Atomic Absorption Spectrophotometry (AAS). Values of sodium and potassium were determined using a Flame photometer using sodium chloride (NaCl) and potassium chloride (KCl) as the standards, while phosphorus was determined using the Vanodo-molybdate method. Chia seeds samples studied revealed the most predominant minerals as phosphorus (531 to 889 mg/100g), calcium (478 to 589 mg/100g), potassium (343 to 526 mg/100g) and, magnesium (322 to 440 mg/100g). The general linear model (GLM) used to determine the coefficient of variation on all chia seed growing sites showed that calcium, iron, and magnesium are the best-performing chia minerals in Kenya and hence should be the minerals of interest in food fortification using chia seeds.
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| 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".