Fructooligosaccharides as molecular markers of geographic origin, growing region, genetic background and prebiotic potential in strawberries: A TLC, HPAEC-PAD and FTIR study
Bibliographic record
Abstract
Strawberries are one of the most consumed fruits worldwide. Scientific research has revealed unique nutraceutical properties, mainly steaming from their content of polyphenols and fructooligosaccharides (FOS), well known prebiotics. Here, we report the fingerprint (chemical composition) of strawberries as molecular markers of geographical origin, growing region, and genetic background, after analyzing different strawberry varieties grown and collected in Mexico and Canada by thin layer chromatography (TLC), high-performance anion-exchange chromatography with pulsed amperometric detection (HPAEC-PAD), and Fourier-transform infrared spectroscopy – principal component analysis (FTIR-PCA). The most abundant carbohydrates in all cases were glucose and fructose. The highest levels of sucrose were observed in Canada-grown strawberries, while Mexico-grown varieties presented the highest FOS abundance. There were clear and distinct patterns correlating FOS isomers determined by HPAEC-PAD and geographic origin. FTIR-PCA second derivative spectra also allowed the classification and differentiation of all strawberry varieties grown in both countries. We suggest that carbohydrates fingerprinting could be associated to specific environmental growing conditions, and not only to genetic makeup.
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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".