Effect of Drying Methods and Ultrasonication in Improving the Antioxidant Activity and Total Phenolic Content of Apple Pomace Powder
Bibliographic record
Abstract
Although extrusion is a promising process to develop ready to eat cereals and snacks, thermal treatment to raw material during extrusion results in degradation of phenolic compounds. Therefore, an approach was made to enhance the total phenolic content (TPC) and antioxidant activity (AA) of apple pomace (AP) prior to extrusion process. In this study, AP powder was naturally fermented (F) for 12 h and then was subjected to ultrasonication (U) at various conditions [25, 37, and 50 µm ultrasonication amplitude (UA) for 1, 2, and 3 min of ultrasonication time (UT)]. AP was then dried in oven (O) and microwave (MW), separately and thus four drying methods, i.e. OF-OU, OF-MWU, MWF-OU, MWF-MWU were used in combinations. Full factorial design was used for experimental plan and results were analyzed using statistical software. It was observed that drying method significantly affected the TPC and AA of AP powder followed by UA. UT did not have any significant effect on TPC, and AA. Maximum TPC, and AA observed for the AP powder dried in MW after fermentation and ultrasonication (MWF-MWU) at 50 µm UA for 3 min UT were 372.98 mg GAE/100g DW, and 729.67 µmol TE/100 g DW, respectively. MWF-MWU drying exhibited a more prominent disrupted and porous structure of AP powder compared with that of OF-OU drying.
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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 | 0.001 |
| 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".