Biochemical Composition Within Coffea arabica cv. Ruiru 11 and Its Relationship With Cup Quality
Bibliographic record
Abstract
<p>Biochemical composition appears to be influenced by both genetic factors and plant growth conditions. The main objective of this study was to evaluate the biochemical composition of selected Ruiru 11 sibs and its relationship with cup quality. Thirty four (34) Ruiru 11 sibs grown in three different locations in Kenya were used in this study. The experiment was laid out in a Randomized Complete Block Design with three replications. Coffee cherries were picked during the peak harvesting period between 2009 and 2011. The cherries were wet processed and graded into different grades based on size, shape and density. Fifty (50) grams of the dry coffee beans per sib per replication were frozen at -80 ºC before grinding (&lt; 0.5 mm particle size) in liquid nitrogen as specified by the Association of Official Analytical Chemists (AOAC). The samples were packed in small plastic bottles and stored at -80 ºC awaiting extraction of biochemical components. Caffeine, trigonelline and total chlorogenic acids were extracted and purified using classical methods and analysed using High Pressure Liquid Chromatography (HPLC). For the lipids, the sample was subjected to Soxhlet extraction using n-hexane. The study demonstrated the existence of high variation in biochemical composition among Ruiru 11 sibs. Significant correlations were observed between biochemical and cup quality traits indicating that biochemical composition plays a major role in determining the sensory quality of coffee. The growing environment was also found to have an effect on biochemical composition as portrayed by high locational variations.</p>
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".