Effect of cooking on the in vitro and in vivo protein quality of soy, oat and wheat varieties
Bibliographic record
Abstract
Abstract Background and Objectives Soy, wheat, and oats are widely consumed crops globally, but variation between cultivars and processing methods can produce products of varying protein quality. This study cooked two different cultivars of wheat, oats, and soy and compared indices of protein quality as well as quantified differences in antinutritional factors. Findings Protein Efficiency Ratio (PER) was highest in cooked soy cultivars and lowest in wheat samples, with cooking having the most impact on soy PER. Protein Digestibility Corrected Amino Acid Score (PDCAAS) of soy was greater than oats which was greater than wheat. Antinutritional factors differed significantly between crops and cultivars, with processing having the greatest impact on soy. In vitro measurements of PDCAAS correlated well with in vivo assessment. Conclusions Thermal processing has variable effects on protein quality depending on crop and cultivar selected, primarily due to differences in amino acid composition. In vitro measurement of protein quality can be used as a rapid screening tool. Significance and Novelty Direct comparison in protein quality between wheat, oat, and soy is rarely reported. Measuring the protein quality of different cultivars and crops pre and postprocessing provides essential data for product development and dietary formulation.
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How this classification was reachedexpand
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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".