Non‐destructive characterization of pulse flours—A review
Bibliographic record
Abstract
The consumption of plant-based proteins sourced from pulses is sustainable from the perspective of agriculture, environment, food security, and nutrition. Increased incorporation of high-quality pulse ingredients into foods such as pasta and baked goods is poised to produce refined food products to satisfy consumer demand. However, a better understanding of pulse milling processes is required to optimize the blending of pulse flours with wheat flour and other traditional ingredients. A thorough review of the state-of-the-art on pulse flour quality characterization reveals that research is required to elucidate the relationships between the micro- and nanoscale structures of these flours and their milling-dependent properties, such as hydration, starch and protein quality, components separation, and particle size distribution. With advances in synchrotron-enabled material characterization techniques, there exist a few options that have the potential to fill knowledge gaps. To this end, we conducted a comprehensive review of four high-resolution nondestructive techniques (i.e., scanning electron microscopy, synchrotron X-ray microtomography, synchrotron small-angle X-ray scattering, and Fourier-transformed infrared spectromicroscopy) and a comparison of their suitability for characterizing pulse flours. Our detailed synthesis of the literature concludes that a multimodal approach to fully characterize pulse flours will be vital to predicting their end-use suitability. A holistic characterization will help optimize and standardize the milling methods, pretreatments, and post-processing of pulse flours. Millers/processors will benefit by having a range of well-understood pulse flour fractions to incorporate into food formulations.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".