Genetic Variability and Correlation of Biochemical and Sensory Characteristics of Coffee
Bibliographic record
Abstract
Organoleptic and biochemical attributes in the coffee bean determine the final cup quality of coffee which is a critical factor in the price determination of coffee in the market. The study aimed at determining the genetic variability of the green coffee bean. The trial sites were located at Siaya and Busia counties in Kenya. Nineteen different genotypes were established and included Arabusta coffee hybrids, backcrosses of Arabica to tetraploid Robusta, Arabica coffee, Robusta coffee, and Arabusta coffee. Randomized Complete Block Design with three replications in each site was used in conducting the experiment. The coffee beans were harvested in the year 2018 and extraction and calculation of sucrose, trigonelline, caffeine, and chlorogenic acids was carried using the recommended methods. The cupping procedure involved the use of five judges in assessing the flavor, aroma, balance, overall standard, acidity, body, and aftertaste of the roasted coffee beans. The sensory evaluation used the Specialty Coffee Association (SCA) method. There were significant variations recorded for the traits that were measured. All the traits were highly heritable registering values of > 50% for heritability whereby, caffeine and oil were highly heritable traits with 90.8% and 88.9% respectively. Oil had a high phenotypic coefficient of variation, genotypic variation, and response values when compared to the other traits. All the organoleptic traits were positively correlated with sucrose, trigonelline, and oil but the correlation with caffeine and chlorogenic acids was negative. The genotypic effects contributed largely to the high heritability recorded with a low influence from the environmental factors.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.001 | 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".