Voltammetric determination of ascorbic acid using carbon paste electrode in ginger samples from selected areas of Ethiopia
Bibliographic record
Abstract
Carbon paste electrode was prepared for the determination of ascorbic acid in ginger (Zingiber officinale) samples from three ginger growing areas (Chilga, Tepi and Dale Sadi) in Ethiopia. The effect of pH on the oxidation response of ascorbic acid in 0.1 M phosphate buffer was investigated and pH 2 was chosen as an optimum value. The oxidation response of ascorbic acid was predominantly diffusion-controlled reaction with a better determination coefficient R2= 0.9945 on the plot of anodic peak current vs square root of scan rate. A square wave amplitude of 45 mV, step potential of 7.0 mV and frequency of 25 Hz were chosen as optimum values. Linear calibration curve in the range of 0.1 – 8.0 mM of ascorbic acid standard in pH 2 phosphate buffer solution was obtained with a determination coefficient of 0.998. The amount of ascorbic acid detected in ginger samples collected from the three areas, Chilga was 6.85, Tepi was 6.59 and Dale Sadi was 6.54 mg/g of ginger powder. Percentage recovery in the range of 93% and 100% was validated for the applicability of the method for the quantitative determination of ascorbic acid in ginger samples. According to Health Canada dietary reference intakes, the maximum amount of ginger powder recommended intake for adult males in Chilga was 13.1, in Tepi 13.7 and in Dale Sadi 13.8 g/day.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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".