Sustainable utilization of apple pomace: Technological aspects and emerging applications
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Apple pomace, a by-product of apple processing, constitutes up to 30 % of the fruit and is rich in dietary fiber, antioxidants, and essential fatty acids, making it a promising resource for value-added applications. Despite its nutritional potential, its high perishability poses significant challenges for reuse. Various techniques, including extrusion, encapsulation, and fortification, have been explored to transform apple pomace into products like enzymes, ethanol, and polysaccharides. Notably, apple pomace has been valorized through pectin extraction for use as a gelling agent in jams and jellies, incorporated into cereal-based products like cookies and bread to enrich dietary fiber content, and subjected to fermentation processes for the production of acetic acid-based products such as vinegar. Drying, particularly non-thermal methods, plays a crucial role in extending its shelf life and preserving nutritional qualities while addressing sustainability goals such as the United Nations 12.3 Goal to reduce food waste. However, non-thermal drying methods face barriers such as high initial investment costs and scalability issues, requiring further optimization. This review evaluates existing and emerging approaches for utilizing apple pomace, focusing on technological advancements in drying methods and their economic, environmental, and quality implications. The findings highlight the need for tailored strategies to overcome practical limitations and maximize the potential of apple pomace as a sustainable resource.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it