Rapid Screening of Natural Liquid Sweeteners by Capillary Electrophoresis
Bibliographic record
Abstract
Capillary electrophoresis (CE) is an instrumental method of chemical analysis that has been developed for the rapid screening of liquid sweeteners (corn syrups, honeys, maple syrups and nectars) from different geographic regions.CE can separate organic compounds in each sweetener sample based on their charge-to-size ratios in a background electrolyte (BGE) solution.Ultraviolet (UV) light absorption can detect the separated compounds for quantitation.Electrophoretic mobility values were determined for all the CE-UV peaks to identify whether flavonoids (e.g., quercetin) and phenolic compounds (e.g., rutin) were present in the sweetener.A novel approach was also developed to perform CE-UV analysis by spiking honeys in the background electrolyte.Due to an increase of the viscosity, both the electroosmotic flow (as indicated by a neutral marker) and the electrophoretic mobility of polydopamine-coated magnetic nanoparticles were decreased by approximately 50%.The electrical conductance was also decreased by approximately 30% due to a higher BGE solution viscosity.These results have demonstrated that CE-UV is a promising technique for the rapid screening of natural liquid sweeteners to detect adulteration by corn syrup.
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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".