Valorization of Pangasius byproduct oil in wax‐structured oleogels as a potential butter substitute for reduced‐fat chocolate‐based products
Bibliographic record
Abstract
Abstract Despite the extensive use of various vegetable oils in oleogel development, sustainable sources, such as belly Pangasius oil derived from industrial byproducts, offer a promising alternative as the liquid phase in oleogel systems. This research aimed to determine the optimal type and concentration of rice bran wax (RBW) and candelilla wax (CW) oleogelators based on Pangasius byproduct oil and to evaluate their application in producing reduced‐fat chocolate‐based products. The selected oleogel systems were further assessed, and the properties of the resulting chocolate products were characterized. The 5% CW oleogel exhibited superior gelling and oil binding capacity (99.86 ± 0.13%). Differential scanning calorimetry of oleogels with 2.5%, 5%, and 7.5% oleogelator concentrations showed a shift in melting points with increasing temperature. Rheological analysis revealed dynamic changes in G' and G" values as the oleogelator concentration increased. Application of the 5% CW oleogel yielded optimal results, producing chocolate products with physical and sensory characteristics comparable to those made with commercial butter. Substituting 50% of the butter with oleogel resulted in products with similar physical attributes and sensory acceptance. Hedonic testing showed no significant differences in most parameters across substitution levels ( p > 0.05), with an average acceptance score exceeding 7 on a 9‐point scale. Notably, the total energy content of the chocolate product was reduced by 43.77% with 100% oleogel substitution. Eventually, this proof of concept highlights the potential of Pangasius byproduct oil as a sustainable liquid phase in oleogel technology, opening new possibilities for its application in confectionery.
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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) | 0.001 | 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".