Bibliographic record
Abstract
This study aimed to investigate the impact of guargum and propylene glycol mono-stearate (PGME) emulsifier on the physicochemical properties of sponge cake.Therefore, guar gum at levels of 0%, 0.3% and 0.5% and PGME emulsifier at 0% and 0.5% levels was added to the formulation.The treatments were examined in 3 replicates and the results obtained were analyzed by using one-way ANOVA and were compared using Duncan's multiple-range test at probability level (0.05>p).The results showed that the addition of guar and PGME led to increased cake specific volume.The results of the texture analysis of the samples indicated a lower hardness (firmness) in the sample containing gum agar (0.3%) and emulsifier (0%).The highest specific volume value of cake belonged to gum (0.5%) and emulsifier (0.5%).An increase in the level of gum agar and PGME emulsifier in the formulation of sponge cake resulted in an improvement in the amount of L component in the cake that can be attributed to the high water absorption capacity by gum and PGME emulsion.Based on the results, the highest sensory attributes score belonged to the sample containing 0.5% gum and 0.5% emulsifier.The addition of agar and PGME emulsifier improved water absorption, increased porosity and improved the texture in such a way that the measurement of texture properties of the cake by texture analyzer revealed that the addition of gum and emulsifiers will reduce the firmness of the cake over time.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.001 |
| Insufficient payload (model declined to judge) | 0.840 | 0.876 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".