Proximate Composition, In Vitro Protein Digestibility, and Micronutrient Density of Commercial Pea, Faba Bean, and Lentil Protein Isolates and Concentrates
Bibliographic record
Abstract
ABSTRACT The nutrient composition and in vitro digestibility of twenty‐seven commercial pulse protein isolates (PI) and protein concentrates (PC) derived from pea, faba, and lentil, and two soy protein isolate controls were tested using consistent analytical methods to understand compositional variability amongst products manufactured by different suppliers. Principal component analysis (PCA) and maximum likelihood factor analysis (MLFA) were applied to model the compositional and amino acid data to determine where the variability between protein products lay. MLFA delineated between isolates and concentrates based upon protein and total dietary fibre content, while moisture and fat variability could be used to differentiate samples within the PI or PC groupings. PCA score charts distinguished between isolates and concentrates due to higher relative concentrations of amino acids in the isolates, with glutamine/glutamic acid contributing to this distinction. Separation according to crop type within PI and PC groupings based upon the arginine and phenylalanine content was also evident. Sodium, potassium, magnesium, and iron micronutrients also contributed to the variability between PI and PC samples. Calculated amino acid scores showed all samples contained sufficient concentrations of essential amino acids to meet FAO requirements established for preschool‐aged children. PI samples had higher in vitro digestibility than PC samples with minimal variability within the groupings.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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 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".