Effects of ultrasonic-assisted osmotic pretreatment on convective air-drying assisted radio frequency drying of apple slices
Bibliographic record
Abstract
Convective air-drying assisted radio frequency drying (CARFD) is an innovative method with the merits of rapid volumetric heating, energy efficiency, as well as high quality food products. However, knowledge about how pretreatment methods impact on the physicochemical characteristics of pretreated apples and the performance of subsequent CARFD is limited. In present work, apple slices were subjected to osmotic pretreatment (OP) and ultrasonic-assisted osmotic pretreatment (USOP) with different intensities (1.0, 2.0 and 3.0 W/g); and the physicochemical properties of pretreated samples (such as mass transfer behavior, water migration status, dielectric loss factor and the others) were compared. The drying duration, energy consumption and a series of quality characteristics (color index, texture, nutrient compositions, antioxidant activity, and so on) of the CARFD produced apple slices were also investigated. The microstructure observations revealed that acoustic waves induced the structural changes and created microchannels. Meanwhile, physicochemical properties of pretreated samples also demonstrated that OP and USOP methods significantly facilitated the water migration status and heightened the dielectric loss factor. In comparison with control and OP methods, three USOP methods were successful in reducing the drying time (14.6%∼27.1%) and total energy consumption (4.5 ∼ 19.8%) of entire processing steps. Concomitantly, USOP (2.0 W/g) samples after drying showed an enhanced quality characteristics in terms of good texture, higher retention of bioactive compounds, higher rehydration ratio, better antioxidant capacity. This study highlights the application of USOP as an effective pretreatment method for enhancing the process efficiency and quality of CARFD produced apple slices.
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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".