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The aim of the study was to determine the sensory quality of the coffee cultivated in the Mantiqueira region of Brazil (Minas Gerais State) and to identify its main descriptors. The sensory quality of red and yellow coffee fruit varieties (Coffea arabica L.) grown in environments with different slopes, at altitudes ranging from 932 to 1,391 m, was analyzed in three different crop seasons. The dry processing method and the wet processing method, based on mechanical removal of skin and mucilage, were used. The variables were analyzed through correspondence analysis. There was no correspondence with sample discrimination between the direction the slope face and coffee sensory profile. The sensory characteristics of coffee such as flavor, acidity, body and sweetness correspond to the cultivation environment with altitudes above 1,050 m. However, for the red coffee fruit varieties, that correspondence only occurred when subjected to a wet‐processing method. The quality of the coffee as a micro‐region product was identified in this study at altitudes above 1,050 m. This effect was not found in natural red coffee fruit varieties. Practical Applications Environmental aspects such as latitude, longitude, altitude and slope, as well as different coffee varieties and processing methods were analyzed in consecutive crop seasons, based on multivariate logistic regression and correspondence analysis techniques. The impacts of different methods of coffee production and processing on beverage quality have been debated for years and will surely continue to be studied in the coming decades, mainly because it is a phenomenon of high complexity. The variations in the sensory profile of coffee produced in different countries or microregions, or even at different planting sites, are noteworthy.

Considering the importance of the chemical compounds in Arabica coffee beans in the definition of the drink sensory quality and authentication of coffee regions, the aim of this study was to evaluate, from principal component analysis-PCA-if there is a relation between the caffeine, trigonelline, and chlorogenic acid (5-CQA) content and the sensory attributes of the drink, and in this context, enabling the differentiation of cultivars in two coffee-producing regions of Brazil. We evaluated seven rust-resistant Coffea arabica cultivars, and two rust-susceptible cultivars in two cultivation environments: Lavras, in the southern region of Minas Gerais state, and Patrocinio in the Cerrado region of Minas Gerais. The flavor and acidity were determinant for differentiation of the cultivars and their interaction with the evaluated environments. Cultivars Araponga MG1, Catigua MG2, and Catigua MG1 are the most suitable for the production of specialty coffee in the state of Minas Gerais. A poor correlation was found between caffeine, trigonelline, 5-CQA contents, and fragrance, flavor, acidity, body, and final score attributes. However, these compounds enabled the differentiation of the environments. The PCA indicated superiority in the sensory quality of cultivars resistant to rust, compared to the control, Bourbon Amarelo, and Topázio MG1190.

The rainfall monitoring allows us to understand the hydrological cycle that not only influences the ecological and environmental dynamics, but also affects the economic and social activities. These sectors are greatly affected when rainfall occurs in amounts greater than the average, called extreme event; moreover, statistical methodologies based on the mean occurrence of these events are inadequate to analyze these extreme events. The Extreme Values Theory provides adequate theoretical models for this type of event; therefore, the Generalized Pareto Distribution (Henceforth GPD) is used to analyze the extreme events that exceed a threshold. The present work has applied both the GPD and its nested version, the Exponential Distribution, in monthly rainfall data from the city of Uruguaiana, in the state of Rio Grande do Sul in Brazil, which calculates the return levels and probabilities for some events of practical interest. To support the results, the goodness of fit criteria is used, and a Monte Carlo simulation procedure is proposed to detect the true probability distribution in each month analyzed. The results show that the GPD and Exponential Distribution fits to the data in all months. Through the simulation study, we perceive that the GPD is more suitable in the months of September and November. However, in January, March, April, and August the, Exponential Distribution is more appropriate, and in the other months, we can use either one.

Automatic classification methods have been widely used in numerous situations and the boosting method has become known for use of a classification algorithm, which considers a set of training data and, from that set, constructs a classifier with reweighted versions of the training set. Given this characteristic, the aim of this study is to assess a sensory experiment related to acceptance tests with specialty coffees, with reference to both trained and untrained consumer groups. For the consumer group, four sensory characteristics were evaluated, such as aroma, body, sweetness, and final score, attributed to four types of specialty coffees. In order to obtain a classification rule that discriminates trained and untrained tasters, we used the conventional Fisher's Linear Discriminant Analysis (LDA) and discriminant analysis via boosting algorithm (AdaBoost). The criteria used in the comparison of the two approaches were sensitivity, specificity, false positive rate, false negative rate, and accuracy of classification methods. Additionally, to evaluate the performance of the classifiers, the success rates and error rates were obtained by Monte Carlo simulation, considering 100 replicas of a random partition of 70% for the training set, and the remaining for the test set. It was concluded that the boosting method applied to discriminant analysis yielded a higher sensitivity rate in regard to the trained panel, at a value of 80.63% and, hence, reduction in the rate of false negatives, at 19.37%. Thus, the boosting method may be used as a means of improving the LDA classifier for discrimination of trained tasters. Key words: Sensory analysis, adaboosting, coffee quality, consumers ResumoOs métodos automáticos de classificação têm sido amplamente utilizados em inúmeras situações, nas quais o método boosting tem se destacado por utilizar um algoritmo de classificação que considera um conjunto de dados de treinamento e, a partir desse conjunto, constrói um classificador com versões reponderadas do conjunto de treinamento. Dada essa característica, esse trabalho tem por objetivo avaliar um experimento sensorial relacionado a testes de aceitação com cafés especiais, tendo como referência grupos de consumidores, treinados e não treinados. Ao grupo de consumidores, foram avaliadas quatro características sensoriais, tais como aroma, corpo, doçura e nota final, atribuídos a quatro tipos de cafés especiais. Com o propósito de obter uma regra de classificação que discrimine provadores treinados e não treinados, utilizaram-se a análise discriminante de Fisher convencional (LDA) e a análise de discriminante via algoritmo de boosting (Adaboost). Os critérios utilizados na comparação das duas abordagens foram sensibilidade, especificidade, taxa de falsos positivos, taxa de falsos negativos e acurácia dos métodos classificatórios. Adicionalmente, para avaliar o desempenho dos classificadores, as referidas taxas de acerto e erro foram obtidas por simulação Monte Carlo, considerando-se 100 réplicas de uma partição aleatória de...

This study was developed to determine the beverage quality and the sensory profile of <em>Coffea arabica</em> accesses of the Germplasm Collection of Minas Gerais State in two consecutive harvests. Mature coffee fruits from 49 accesses were selectively harvested in the 2015 and 2016 harvests, and after processing and drying they were sensorially evaluated by a team of trained judges in accordance to the protocols of the Specialty Coffee Association. The data were analyzed by Principal Component Analysis, Cluster Hierarchical Analysis, and Content Analysis. The year of harvest influences the scores of the sensory attributes of the beverage of some coffee accesses. The access 27 (Pacamara) stood out with the highest sensory score seen in the first year of harvest. The accesses 36 (Timor Hybrid UFV376-52) and 43 (BE5 WushWush) stood out for the final sensory score in the two years of evaluation. All accesses belonging to the Timor Hybrid Germplasm stood out in the beverage quality in the second year of harvest. A total of 139 sensory attribute descriptors were identified, and the content analysis showed different levels for the frequency of terms in each access group formed as a function of quality. The Germplasm Collection of Minas Gerais has a wide variability for the production of superior quality coffees, and the year of harvest influences to a greater or lesser degree the potential of quality of the accesses.

Sensory analysis of cafes assumes that a sensory panel is formed by trained panelists according to recommendations of the American Specialty Coffee Association. However, the choice that determines the preference of a coffee is routinely done through experimentation with consumers, in which largely presents no particular skill in terms of sensory characteristics. Upon this fact, this study aimed to conduct a study considering several probabilistic distributions belonging to the class of generalized extreme value, considering a sensory analysis applied to evaluation of four specialty coffees produced with different processes and at different altitudes in the mountain region of the Mantiqueira state of Minas Gerais. For this analysis, we considered a sensory panel trained to untrained consumers. It was found that the extreme value distribution was the best fit and the final note that the odds of a consumer to submit a maximum score was 9.0 points lower. Therefore, there is evidence to conclude that an efficient identification of specialty coffees produced in this region made by consumers requires more intensive training.