Fatty Acids Profile of Coffea arabica L. Resistant to Leaf Rust Grown in Two Environments of Minas Gerais, Brazil
Bibliographic record
Abstract
The quality of the coffee drink is defined by its sensorial attributes which are developed through the chemical predecessors found on raw grains. Due to this relation, this present work had a goal evaluating the sensorial characteristics of resistant coffee cultivars to leaf rust for the production of specialty coffee and correlate them with the profile of fatty acids present on raw grains, enabling the distinction of cultivars in two environments of Minas Gerais. For this purpose, seven cultivars of Coffea arabica resistance to leaf rust were evaluated and two susceptible cultivars, in two growing environments: Lavras, in the South of Minas Gerais and Patrocínio, in the Cerrado of Minas Gerais. The C16:0, C16:1, C18:0, C18:1, C18:2 w-6, C18:3 w-3 fatty acids common in coffee grains, were identified in the cultivars. The presence of an acid uncommon in coffee grains, the gamma linolenic acid (C18:3 w-6, GLA) was also detected. The palmitic and alpha linolenic fatty acids presented correlation with the sensorial quality of the drink. The linoleic, palmitic, alpha linolenic and gamma linolenic fatty acids were capable to differentiate the Patrocinio and Lavras environments. The profile of the fatty acids permitted differentiate the leaf rust resistant cultivars, the environments as also the interaction between these two matters related to the sensorial quality.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 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".