Proximate Composition, In Vitro Protein Digestibility, and Micronutrient Density of Commercial Pea, Faba Bean, and Lentil Protein Isolates and Concentrates
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 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.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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 it