A micro-method to determine quinoa (<i>Chenopodium quinoa</i> Willd.) grain saponins and sapogenins contributing to bitterness
Bibliographic record
Abstract
A microextraction method was optimized to determine and characterize saponins in very small sample of quinoa ( Chenopodium quinoa Willd.) seeds (0.1 g) with minimal amount of solvent (2 mL). This rapid microextraction method can be used to screen and select quinoa genotypes in early stages of crop improvement and genetic mapping studies, where seed supply is very limited. The optimized microextraction method combined with vanillin–sulphuric acid color development method revealed 2.2 ± 0.85 to 2.4 ± 0.57 mg g −1 (0.22%–0.24%) oleanolic acid (OA) equivalent g −1 sample in sweet quinoa while bitter quinoa seeds had higher concentrations of 4.7 ± 0.37 to 7.34 ± 0.59 mg of OA equivalent g −1 of the sample (0.47%–0.73%). Afrosimetric method based on the foam produced by sapogenins, showed, 0.1–0.6 mg g −1 saponin in sweet quinoa genotypes compared to 1.52 ± 0.3 to 1.86 ± 0.1 mg g −1 saponin in bitter genotypes. Correlation analysis revealed a significant correlation between foam and the vanillin sulphuric acid method for saponin determination ( r 2 = 0.967, p < 0.001). Four trimethylsilylated sapogenins (OA, hederagenin, serjanic acid, and phytolaccagenic acid) were separated using gas chromatography-mass spectrometry (GCMS) to assess compositional differences in the bitter and sweet quinoa seeds. MVC 269 with the most OA equivalent value and the highest foam also showed maximum intensity for all sapogenin analyzed in this study, followed by MVC 277. The sweet quinoa genotype (MVC 204) showed the lowest intensity profile for all sapogenins.
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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".