Bibliographic record
Abstract
Food is fundamental to life. It is the source of essentially all the chemical and biological components found in our bodies. Given its importance, there is a growing desire among food producers, consumers, nutritionists, and dieticians to have a better understanding of the precise chemical content of foods. Unfortunately, the chemical composition of most foods is not well known. Indeed, standard food composition tables only provide data on a few dozen highly abundant chemicals. However, recent advances in analytical chemistry technologies and in the field of metabolomics now make it possible to identify and quantify thousands of compounds in biological matrices. These developments suggest that it may be possible to use metabolomics to more completely characterize the chemical constituents in food. The central objectives of my thesis are: 1) to apply modern quantitative metabolomic methodsto identify and quantify the chemical constituents and micronutrients in a select number of Alberta-grown vegetables, fruits, cereals and meats; and 2) to create a fully web accessible database that contains bothexperimentally derived values and literature-derived information on Alberta-grown foods, called the “Alberta Food Composition Database” (AFCDB: http://afcdb.ca). In working towards Objective #1, a combination of several modern metabolomics techniques, including ICP-MS, DFI-MS/MS, GC-MS, HPLC, and NMR were used to characterize the chemical constituents of nearly 40 different, Alberta-grown food products. Sample preparation, extraction, and separation techniques were developed or optimized to characterize amino acids, fatty acids, trace metals, vitamins, organic acids, phytochemicals, sugars, and lipids. ICP-MS assays generated composition data for up to 54 metal ions. DFI-MS/MS assay yielded data on about 50-110 compounds per food sample, whilethe GC-MS- ii based assays generated data for about 30-75 non-volatile compounds, 20-40 volatile compounds, and up to 20 fatty acids for each food sample. NMR assays yielded data on 30-50 compounds per food sample. Detailed literature mining led to the identification of up to 2000 more compounds for certain food products. By completing this study, I believe I have helped create perhaps the most comprehensive food information resource in the world. Through the AFCDB, Alberta producers have access to some of the most detailed and information-rich data on the food products they produce. This work could lead toa paradigm shift for food-health labeling, making Alberta food products uniquely appealing for health conscious consumers.
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 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".