Multi-frequency power ultrasound as a novel approach improves intermediate-wave infrared drying process and quality attributes of pineapple slices
Bibliographic record
Abstract
This study evaluated the effect of mono-frequency ultrasound (MFU, 20 kHz), dual-frequency ultrasound (DFU, 20/40 kHz), and tri-frequency ultrasound (TFU, 20/40/60 kHz) on mass transfer, drying kinetics, and quality properties of infrared-dried pineapple slices. Pretreatments were conducted in distilled water (US), 35 °Brix sucrose solution (US-OD), and 75% (v/v) ethanol solution (US-ET). Results indicated that ultrasound pretreatments modified the microstructure of slices and shortened drying times. Compared to the control group, ultrasound application reduced drying time by 19.01-28.8% for US, 15.33-24.41% for US-OD, and 38.88-42.76% for US-ET. Tri-frequency ultrasound provoked the largest reductions, which exhibited time reductions of 6.36-11.20% and better product quality compared to MFU. Pretreatments increased color changes and loss of bioactive compounds compared to the control but improved the flavor profile and enzyme inactivation. Among pretreated sample groups, US-OD slices had lower browning and rehydration abilities, higher hardness values, and better retention of nutrients and bioactive compounds. Therefore, the combination of TFU and osmotic dehydration could simultaneously improve ultrasound efficacy, reduce drying time, and produce quality products.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.000 | 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 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".