Effect of Processing Methods on the Physicochemical, Mineral and Carotene Content of Orange Fleshed Sweet Potato (OFSP)
Bibliographic record
Abstract
The effect of processing methods on the physiochemical, mineral, vitamin C and carotenoid content of orange fleshed sweet potatoes were investigated. The processing methods used were boiling, steaming, roasting, frying and microwaving. The result of the proximate composition showed that the roasted orange fleshed sweet potatoes (OFSP) had the highest ash content ranging from 0.32-0.99%, crude protein 0.96-3.12%, crude fiber 0.50-3.40% and carbohydrate content 13.98-40.10% with a decrease in the moisture content from 83.10% - 49.25%. Fat content of the fried OFSP ranging from 0.96-6.01% was higher than the other samples. Steaming method enhanced the vitamin C content of the OFSP when compared to other processing method, while carotenoid losses were higher after frying 2.59mg/g, than after microwaving 3-91%, roasting 4.73mg/g, boiling 4.60mg/g and steaming 2.68mg/g. Mineral analysis showed that the boiled orange flesh sweet potatoes (OFSP) had zinc, copper and magnesium content higher than the other heat treated samples with 6.21mg/g, 4,164mg/100g and 479.88mg/100g respectively. Sensory analysis results showed that there were no significant (p<0.05) differences in the sensory scores of the orange-fleshed sweet potatoes. The study therefore showed that roasting and frying made available more protein, fat, ash and carbohydrate content, while boiling made available more minerals.
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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) | 0.000 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".