Distinguishing Vaccinium Species by Chemical Fingerprinting Based on NMR Spectra, Validated with Spectra Collected in Different Laboratories
Bibliographic record
Abstract
A method was developed to distinguish Vaccinium species based on leaf extracts using nuclear magnetic resonance spectroscopy. Reference spectra were measured on leaf extracts from several species, including lowbush blueberry (Vaccinium angustifolium), oval leaf huckleberry (Vaccinium ovalifolium), and cranberry (Vaccinium macrocarpon). Using principal component analysis, these leaf extracts were resolved in the scores plot. Analysis of variance statistical tests demonstrated that the three groups differ significantly on PC2, establishing that the three species can be distinguished by nuclear magnetic resonance. Soft independent modeling of class analogies models for each species also showed discrimination between species. To demonstrate the robustness of nuclear magnetic resonance spectroscopy for botanical identification, spectra of a sample of lowbush blueberry leaf extract were measured at five different sites, with different field strengths (600 versus 700 MHz), different probe types (cryogenic versus room temperature probes), different sample diameters (1.7 mm versus 5 mm), and different consoles (Avance I versus Avance III). Each laboratory independently demonstrated the linearity of their NMR measurements by acquiring a standard curve for chlorogenic acid (R(2) = 0.9782 to 0.9998). Spectra acquired on different spectrometers at different sites classifed into the expected group for the Vaccinium spp., confirming the utility of the method to distinguish Vaccinium species and demonstrating nuclear magnetic resonance fingerprinting for material validation of a natural health product.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".