Modelling of nutrient bioaccessibility in almond seeds based on the fracture properties of their cell walls
Bibliographic record
Abstract
The cell walls (dietary fibre) of edible plants, which consist of mainly non-starch polysaccharides, play an important role in regulating nutrient bioaccessibility (release) during digestion in the upper gastrointestinal tract. Recent studies have shown that structurally-intact cell walls hinder lipid release from the parenchyma cells of almond seeds. A theoretical model was developed to predict the bioaccessibility of lipid using simple geometry and data on cell dimensions and particle size for calculating the number of ruptured cells in cut almond cubes. Cubes (2 mm) and finely-ground flour of low and high lipid bioaccessibility, respectively, were prepared from almond cotyledons. The model predictions were compared with data from in vitro gastric and duodenal digestion of almond cubes and flour. The model showed that lipid bioaccessibility is highly dependent on particle size and cell diameter. Only a modified version of the model (the Extended Theoretical Model, ETM), in which the cells at the edges and corners were counted once only, was acceptable for the full range of particle sizes. Lipid release values predicted from the ETM were 5.7% for almond cubes and 42% for almond flour. In vitro digestion of cubes and flour showed that lipid released from ruptured cells was available for hydrolysis and resulted in lipid losses of 9.9 and 39.3%, respectively. The ETM shows considerable potential for predicting lipid release in the upper gastrointestinal tract. Further work is warranted to evaluate the efficacy of this model to accurately predict nutrient bioaccessibility in a broad range of edible plants.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".