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Item Sample size estimation of fruit maturation for Arabica’s coffee(Instituto Agronômico (IAC), 2025-01-31) Botega, Gustavo Pucci; Abrahão, Juliana Costa de Rezende; Botelho, Thiago Tavares; Botelho, Cesar Elias; Salvador, Guilherme Soares; Gonçalves, Flávia Maria AvelarThis study aimed at establishing the ideal sample size for evaluating the maturation cycle in Coffea arabica, and investigating the errors associated with different sample sizes, in addition to verifying the possibility of using the clustering method to separate genotypes according to the maturation stage. Two experiments were analyzed: one with F2:3 progenies using visual maturation assessment through fruit counting, and another with cultivars using image processing for maturation assessment. To determine the ideal sample size for this trait, we used the estimation of the errors associated with maturation, using the bootstrap technique. Subsequently, the K-means algorithm was tested as an alternative for clustering genotypes into maturation classes. The application of the bootstrap technique in order to estimate the error associated with maturation revealed that the adoption of a 450-mL sample size resulted in an associated error of approximately 5%, indicating that it is an adequate size for character assessment. The implementation of K-means as a clustering tool offers a promising perspective for Arabica coffee plant breeding programs. A more comprehensive analysis, which not only assesses the proportion of ripe fruits, but also considers the distribution of different maturation stages, provides a more accurate understanding of the maturation process. This allows a more precise identification of genotypes with the most suitable performance for different growing conditions, as well as enabling adjustments in harvest management and post-harvest processing, optimizing coffee quality.Item An index to evaluate the acceptance of specialty coffees in consumer groups(Associação Brasileira de Engenharia Agrícola, 2020) Resende, Mariana; Cirillo, Marcelo Â.; Borém, Flávio M.Numerous factors are related to the individual sensory perception of consumers, which makes it impossible to adapt a model that explains their behavior. In this context and given the scarcity of statistical indexes that evaluate preferences for specialty coffees, new statistical methods should be studied. To this end, our study aimed to create an index that measures the acceptance of specialty coffees. The index was built considering the fit of regression models as a function of principal component scores. Validation was done by significance tests, whose probabilities were obtained by bootstrapping, considering the main measures used in diagnosing outliers as weights, with application to real data from different consumer groups. The coffee varieties Acaia and Bourbon were discriminated in relation to altitude. In conclusion, the index was adequate for the analysis and characterization of specialty coffees grown at different altitudes.