Micromeritic, thermal, dielectric, and microstructural properties of legume ingredients: A review
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.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".