Determination of the protein quality of almonds (<i>Prunus dulcis</i> L.) as assessed by in vitro and in vivo methodologies
Bibliographic record
Abstract
), such as all nuts, are positioned within the protein foods grouping within the current U.S. Dietary Guidelines. The ability to make claims related to the protein content of almonds, within the United States, requires substantiation via the use of the Protein Digestibility-Corrected Amino Acid Score (PDCAAS). The present study was designed to provide current estimates of PDCAAS, using both in vivo and in vitro assays, of key almond varietals from the 2017 California harvest. Additionally, historical protein and amino acid composition data on 73 separate analyses, performed from 2000 to 2014, were analyzed. Amino acid analysis confirmed lysine as the first-limiting amino acid, generating amino acid scores of 0.53, 0.52, 0.49, and 0.56 for Butte, Independence, Monterey, and Nonpareil varietals, respectively. True fecal protein digestibility coefficients ranged from 85.7% to 89.9% yielding PDCAAS values of 44.3-47.8, being highest for Nonpareil. Similar, albeit lower, results were obtained from the in vitro assessment protocol. Analysis of the historical data again positioned lysine as the limiting amino acid and yielded information on the natural variability present within the protein and amino acid profiles of almonds. Comparison of the 2017 AA profile, averaged across almond varietals, to the historical data provided strong evidence of persistence of amino acid composition and indices of protein quality over time.
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.
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) | 0.001 | 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 | 0.001 |
| 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".