Bioactive Compounds Profile of Solok Arabica Coffee Analyzed by GC-MS Method
Bibliographic record
Abstract
The type of coffee affects the compounds in it, the environment, and the soil. One of the areas in West Sumatera where a Coffee Producer is Solok Regency. The kind of coffee that is cultivated in Solok is Arabica coffee. The height of the planting point has an influence on the taste, so Solok Arabica coffee has a different taste from the flavors of other Arabica coffees that are widely spread throughout Indonesia. This study aims to determine the compounds contained in Solok Arabica coffee, which was roasted at 200oC for 10 minutes. The compound detection in Solok Arabica coffee was carried out using gas chromatography-mass spectrometry (GC-MS). The results of GC-MS analysis detected 25 compounds in Solok Arabica coffee at 200oC for 10 minutes, and 4 of them were detected in large quantities, namely pyridine, caffeine, n-hexadecanoic acid, and butyl 9,12-octadecadienoate with amounts between 70-97 m / z. Pyridine is a benzene derivative by replacing CH groups with N atoms, which are toxic to humans because they can cause nausea, vomiting, headache, dizziness, and irritation when in contact with the skin. Caffeine is the main bioactive component of the purine ring system in coffee. The sensory test method used to determine the typical Arabica Coffee of “Ranah Minang” is cupping to assess the taste of the coffee. n-hexadecanoic acid is a saturated fatty acid with antioxidant, hypocholesterolemic, nematicide, anti-androgenic, hemolytic, pesticide, lubricant, 5-alpha reductase inhibitor, antipsychotic, and anti-inflammatory activity.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".