Improving the Frying Stability of Peanut oil through blending with Palm kernel Oil
Bibliographic record
Abstract
The present work explores the possibility of improving the frying stability of peanut oil, by decreasing its level of unsaturation using tropical oil. Blends comprising of 35.92-46.63% oleic acid, 17.74-25.41% linoleic acid, and less than 0.01% linolenic acid were studied. The fatty acid compositions were attained by blending peanut oil (PNO) and palm kernel oil (PKO) at 90:10; 80:20; 70:30; and 60:40 ratios respectively. The blends were used to fry sliced yam and subsequently subjected to chemical analyses while the fried yam slices were subjected to sensory evaluation. Pure peanut oil was also used to fry sliced yam, and served as control. Findings from this study indicate that the blends recorded lower values of total polar compounds (7.90-14.60%) than the control (15.40%); and lower values of FFA (0.90-1.45% vs. 1.09% for the control) with the exception of the 60:40 blend which recorded FFA value of 1.45%. In terms of acceptability of taste, flavour and overall acceptability, yam slices fried in the control oil were generally preferred over those fried in blends; however among the blends, slices fried in 90:10 and 80:20 blends recorded the highest scores for overall acceptability and were preferred by the panelists more than those fried in the 70:30 and 60:40 blends. In terms of acceptability of appearance no significant difference was obtained for slices fried in the different blends. Findings from this work further suggest that peanut oil for frying purpose can be substituted with PNO/PKO blends of up to 80:20 ratio.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| 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".