Micromeritic, thermal, dielectric, and microstructural properties of legume ingredients: A review
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Abstract The legume‐based food market has grown consistently in recent years because of the high global demands for plant‐based proteins. Since isolation of proteins produces the same volume of starch and large quantities of fiber‐rich fractions, these ingredients require property measurements for their industrial applications. Size‐reduction operations separate the ingredients from the legume grains by creating a large surface area with a definite size. Knowledge of micromeritic properties of legume‐based flour ingredients is indispensable in the design of process equipment and logistic operations. This review covers the particle‐size distributions of legume flours with a desired particle size that fits the food industry and fulfills the nutritional requirements of consumers. It focuses on the strict particle‐size requirement in the legume industry to obtain consistent ingredients for diverse food applications. Furthermore, engineering properties of legume ingredients, including micromeritic, dielectric, structural (e.g., Fourier transform infrared [FTIR], X‐ray diffraction [XRD], and scanning electron microscopy [SEM]), and thermal (e.g., thermal conductivity, diffusivity, heat capacity, glass transition, and melting temperature) properties and their interrelationships, have been discussed.
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.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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 it