Characterization of the mechanical properties of high-moisture meat analogues using low-intensity ultrasound: Linking mechanical properties to textural and nutritional quality attributes
Bibliographic record
Abstract
Plant-based meat analogues offer possible alternatives to meat consumption. However, many challenges remain to produce a palatable meat analogue as well as to understand the roles of different processing steps and ingredients on both the texture and nutritional properties of the final product. The goal of this paper is to help with addressing these challenges by using a low-intensity ultrasonic transmission technique, both online and 24 h after production, to investigate high-moisture meat analogues made from a blend of soy and wheat proteins. To understand the ultrasonic data in the context of traditional characterization methods, physical properties (meat analogue thickness, density, peak cutting force) and protein nutritional quality attributes of the meat analogues were also characterized separately. The ultrasonic velocity was found to decrease with the feed moisture content and to be strongly correlated (r = 0.97) with peak cutting force. This strong correlation extends over a wide range of moisture contents from 58% to 70%, with the velocity decreasing from about 1730 m/s to 1660 m/s over this range. The protein quality was high for all moistures, with the highest amino acid score and in vitro protein digestibility being observed for the highest moisture content treatment. The accuracy of the ultrasonic measurements was enhanced by the development of an innovative non-contact method, suitable for materials exhibiting low ultrasonic attenuation, to measure the meat analogue thickness ultrasonically and in a sanitary fashion - an advance that is potentially useful for online monitoring of production problems (e.g., extruder barrel-fill and cooling-die temperature issues). This study demonstrates, for the first time, the feasibility of using ultrasonic transmission techniques to measure both velocity and sample thickness simultaneously and provide information in real time during production that is well correlated with some textural and nutritional attributes of meat analogues.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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 teacher head, 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".