Impact of cooking on the protein quality of Russet potatoes
Bibliographic record
Abstract
Despite being low in crude protein, on a fresh weight basis, given their overall contribution to the North American diet, potatoes contribute approximately 2%-4% of the population's protein intake. However, the quality of the protein remains ill-defined. To that end, Russet potatoes were secured and subjected to various cooking conditions (raw [control], boiled, baked, microwaved, and fried [3, 6, and 9 min]) to determine the impact of cooking method on protein quality, as determined by amino acid score (AAS) and indices of in vivo true fecal protein digestibility (TFPD%; rodent bioassay) and in vitro protein digestibility (pH-drop, pH-Stat, and simulated gastrointestinal digestion both static and dynamic). The AAS of raw Russet potatoes was 0.67 ± 0.01, with histidine being the limiting AA. Frying led to a significant reduction in the AAS, however, other cooking methods yielded similar results to the raw control. The TFPD% of raw potato was low (40.5% ± 3.9%) and was significantly enhanced to over 80% with all cooking methods. Similar patterns were observed with all in vitro measures, however, all methods yielded higher values for the raw control samples. Final protein digestibility-corrected AAS (PDCAAS; product of AAS and TFPD%) values ranged from 0.27 (raw) to a high of 0.57 (boiled), with cooked values being comparable to other plant-based protein sources, including grains, and some nuts and pulses. In vitro PDCAAS values followed similar trends. This study defined the protein quality of cooked Russet potatoes and provides data for use in defining the quality of total protein consumed in the North American diet.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".