Potential of fluorescence spectroscopy for the characterisation of maple syrup flavours
Bibliographic record
Abstract
BACKGROUND: Maple syrup has high maket value. It is produced in North East America from the heat-evaporated sap of Acer saccharum Marshall. For marketing purposes, there is interest in defining its flavour profile in a consistent and repeatable manner. An experiment was undertaken to explore the potential of autofluorescence of maple syrup induced at 275 and 360 nm to characterise flavours. RESULTS: A mixed data factor analysis revealed two independent groups of variables. One represents early season woody and late season empyreumatic flavours. The other is related to off-flavour, confectionery and maple flavours. Maple and confectionery flavours are subtle, difficult to distinguish and opposed to off-flavour. There were clear relationships among the two groups and fluorescence profiles. For each of the five basic flavours, discriminant models based on partial least squares regressions were developed. For each sample of syrup, flavours combined to form flavour profiles, and the results from the five discriminant models were aggregated to reproduce these profiles. For excitation at 275 nm, the woody/off-flavour and confectionery/empyreumatic/maple flavour profiles were classified correctly 86 and 78% of the time (cross-validation) respectively. CONCLUSION: Induced autofluorescence spectra were shown to contain information related to maple syrup flavours. This fluorescence-flavour relationship is not considered quantitative yet, and further research avenues are proposed.
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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 | 0.001 |
| 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 | 0.001 |
| 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".