Comparison of Ultrasonic Velocities in Dispersive and Nondispersive Food Materials
Bibliographic record
Abstract
Ultrasonic techniques are increasingly being used to evaluate the properties of food materials. Interpretation of the structure and dynamics on the basis of measured ultrasonic parameters requires rigorous definition of ultrasonic parameters such as velocity, especially since many food materials can display considerable dispersive behavior (changes in velocity with frequency). Agar gel (2% w/v) and agar gel (2% w/v) with a regular array of bubbles (8% volume fraction) were chosen as nondispersive and dispersive materials, respectively. Frequency and time domain techniques were used to analyze velocities. Signal, phase, and group velocities were identical in the agar gel and were indistinguishable from those of water (1500 m s(-1)), indicating the predominant effect of the bulk modulus of the water they contain on the longitudinal modulus of the gel. In contrast, the inclusion of the bubbles in the agar gel led to strongly dispersive behavior, with group velocities varying by 1000 m s(-1) above and below the 1500 m s(-1) of the agar gel without bubbles, depending on frequency. The addition of bubbles also led to strong attenuation in the agar gel with a peak occurring at a frequency associated with a band gap arising from destructive interference of sound waves. The results show that care must be taken when comparing ultrasonic parameters derived from experiments on food materials performed at different frequencies or with different ultrasonic techniques.
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| 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".