A high-throughput plasmonic tongue using an aggregation assay and nonspecific interactions: classification of taste profiles in maple syrup
Bibliographic record
Abstract
A simple colorimetric test detects off-flavour profiles of maple syrups in minutes, which are detectable by the naked eye. As flavour profiles are due to complex mixtures of molecules, the test uses nonspecific interactions for analysing the aggregation and color change of Au nanoparticles (AuNPs) induced by the different organic molecules contained in off-flavour maple syrup. The test was optimal with 13 nm citrate-capped AuNPs reacting 1 : 1 with pure maple syrup diluted 10 times. Under these conditions, normal flavour maple syrups did not react and the solution remained red, while off-flavoured maple syrups aggregated the AuNPs and the solution turned blue. Different classes of molecules were then tested to evaluate the types of compounds typically found in maple syrups reacting in the test, showing that sulfur- and amine-containing amino acids and aromatic amines caused aggregation of the AuNPs. The test was validated with 1818 maple syrup samples from the 2018 harvest in Quebec and 98% of the off-flavoured maple syrups were positively identified against the standard taste test. Preliminary tests were performed on site in maple sugar shacks to validate the applicability of the test on the production site.
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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 | 0.001 | 0.000 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".