BAKING AND SENSORY CHARACTERISTICS OF MUFFINS INCORPORATED WITH APPLE SKIN POWDER
Bibliographic record
Abstract
ABSTRACT Apple fruit skin, a rich source of dietary fiber and phenolics, is a by‐product of apple processing. The feasibility of incorporating dried apple skin powder (ASP) as a value‐added food ingredient in bakery food products using a model system of muffins was investigated. The blanched, dehydrated and ground ASP was incorporated into muffins at 0, 4, 8, 16, 24 or 32% (w/w) levels with replacement of equivalent amount of wheat flour of a standard muffin mixture. The highest level of replacement (32% w/w) had a significant adverse effect on the baking characteristics. A taste panel of 66 panelists showed that the replacement of wheat flour with 8, 16 or 24% ASP in the muffin mixture did not affect the overall acceptability. PRACTICAL APPLICATIONS Identification of ways to incorporate apple skins, one of the by‐products of apple pie and sauce manufacturing, as a health food ingredient in human diet could provide many health benefits. Furthermore, better use of the by‐product will also provide benefits to the apple industry as well as solutions for environment concerns associated with disposal. The results of the present study indicate that blanched and dehydrated apple skin powder (ASP) could be considered as an alternative dietary fiber source or specialty food ingredient for muffins. Therefore, the potential for the industrial exploitation of ASP as a health food ingredient for the bakery industry and selected functional foods is promising.
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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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".