Berry pomace as a potential ingredient for plant‐based meat analogs
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
Abstract Given the projected global population growth and the associated increase in demand for sustainable and nutritious food options, plant‐based meat analogs are increasingly popular. Berry pomace, a by‐product of the juice and wine industry, emerges as a promising ingredient for enhancing these products. This paper comprehensively explores the innovative use of berry pomace in the development of plant‐based meat analogs. It highlights key components such as antioxidants, natural colorants, dietary fibers, oils, and micronutrients, which significantly contribute to enhancing the nutritional profiles, sensory qualities, and shelf stability of these analogs. Methods for incorporating berry pomace into plant‐based meats, including direct addition and the addition of berry pomace extract using innovative technologies, such as high‐moisture extrusion, 3D printing and emulsion methods, are discussed. Moreover, the challenges of integrating berry pomace into plant‐based meats are critically analyzed, focusing on variability in pomace composition, potential sensory impact, and the technological adaptations required for optimal use in food production. The potential of berry pomace to enhance both the quality and appeal of plant‐based meats is highlighted, underscoring its significant contribution to the development of more sustainable food systems by valorizing food waste to high‐value products.
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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 | 0.000 | 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) | 0.000 | 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