Free Galactose Content in Selected Fresh Fruits and Vegetables and Soy Beverages
Bibliographic record
Abstract
The free galactose content was determined in three soy beverages, and 34 selected fruits and vegetables purchased at different times of the year and/or local markets in British Columbia, Canada. The objective of the work was to provide additional information on the free galactose content of foods to assist individuals with galactosemia in making dietary decisions. Free galactose contents in the selected plant materials ranged from 2.0 +/- 0.1 mg/100 g in red potato to 39.7 +/- 1.9 mg/100 g in red pepper. Different time of the season, variety, and storage of the product affected the free galactose contents in most of the plant materials measured in this study. Free galactose levels in kiwi, green seedless grapes, and bell peppers were found to be higher than previous reports, whereas the amount of free galactose in three varieties of tomatoes was significantly lower than previously reported. An evaluation of the change of galactose in Roma tomatoes during ripening showed that free galactose levels increased linearly over time, and storage at 4 degrees C significantly increased free galactose levels in tomatoes. Soy beverages made from soy protein isolate contained less free galactose (1.3 +/- 0.2 mg /100 g) compared to the samples made from whole soybeans (4.8 +/- 1.9 and 5.3 +/- 1.7 mg/ 100 g). This study provides additional information on the range of free galactose in fruits and vegetables which will allow individuals with galactosemia to make more informed dietary choices.
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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".