Analysis on the Quality Change of Tempeh, Catfish and Fried Chicken as the Effect of the Repetitive Used Cooking Oil
Bibliographic record
Abstract
The aim of this study is to analyze the change of product characteristic that will be fried using the cooking oil that is repetitively used by the sidewalk vendors (locally abbreviated as PKL). In this study, Randomized Complete Block Design (RCBD) was used and divided into 2 factors: types of product consisting of 3 levels (fried chicken, fried catfish and fried tempeh (Indonesian dish made of deep-fried fermented soya beans)) and frying frequency (control, 4th frying, 6th frying and 8th frying). Once obtained, the data would be analyzed using ANOVA (Analysis of variance) method in which if an interaction is found, it would be continued by DMRT (Duncan’s Multiple Range Test) using confidence interval at 5%. The result of the research shows that the types of product and the usage of repetitively used cooking oil at the different frequencies will bring an effect on the product quality after a repetitive frying by causing a significantly different impact (? = 0. 05) on water level, nutrient level, peroxide number, TBA level, p-Anisidine number at 0, 38, and free fatty acid levels.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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".