Determination of the Protein and Amino Acid Content of Fruit, Vegetables and Starchy Roots for Use in Inherited Metabolic Disorders
Bibliographic record
Abstract
Amino acid (AA)-related inherited metabolic disorders (IMDs) and urea cycle disorders (UCDs) require strict dietary management including foods low in protein such as fruits, vegetables and starchy roots. Despite this recommendation, there are limited data on the AA content of many of these foods. The aim of this study is to describe an analysis of the protein and AA content of a range of fruits, vegetables and starchy roots, specifically focusing on amino acids (AAs) relevant to AA-related IMDs such as phenylalanine (Phe), methionine (Met), leucine (Leu), lysine (Lys) and tyrosine (Tyr). AA analysis was performed using high-performance liquid chromatography (HPLC) on 165 food samples. Protein analysis was also carried out using the Dumas method. Foods were classified as either 'Fruits', 'Dried fruits', 'Cruciferous vegetables', 'Legumes', 'Other vegetables' or 'Starchy roots'. 'Dried fruits' and 'Legumes' had the highest median values of protein, while 'Fruits' and 'Cruciferous vegetables' contained the lowest median results. 'Legumes' contained the highest and 'Fruits' had the lowest median values for all five AAs. Variations were seen in AA content for individual foods. The results presented in this study provide useful data on the protein and AA content of fruits, vegetables and starchy roots which can be used in clinical practice. This further expansion of the current literature will help to improve diet quality and metabolic control among individuals with AA-related IMDs and UCDs.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| 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) | 0.001 | 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".