Exploring the Composition and Authenticity of Honey and Syrup Samples Using Quantitative NMR Spectroscopy and Principal Component Analysis in an Upper-Year Undergraduate Analytical Environmental Course
Bibliographic record
Abstract
The adulteration of food products, including honeys and syrups, is of growing concern in global food markets. Detecting adulterated food products can be difficult using traditional analytical methodologies because of complex sample matrices or poor separation of key compounds. NMR is an analytical tool that circumvents some of these issues by providing a snapshot of chemical components in a sample mixture with minimal and unbiased sample preparation. In this upper-year undergraduate and graduate laboratory, students are introduced to NMR as an analytical tool through the analysis of a honey or syrup sample of their choice. Simple sugars in honeys or syrups are quantified using a single point internal standard, while the NMR signals arising from the complex organic mixture of amino acids, proteins, carbohydrates, and oils found in these syrups are compared against a sample data set using principal component analysis. The experiment highlights the benefits and challenges of NMR as an analytical tool, demonstrating the simplicity of sample preparation and single-point calibration, as well as the limitations of instrument sensitivity and signal resolving power.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".