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Item Fermented natural coffee followed by pulping: Analysis of the initial sensory quality and after six months of storage(Universidade Federal de Lavras, 2023-09-01) Salvio, Luís Gustavo Amaral; Cirillo, Marcelo Ângelo; Borém, Flávio Meira; Alves, Ana Paula de Carvalho; Palumbo, Juliana Maria Campos; Santos, Cláudia Mendes dos; Haeberlin, Luana; Schwan, Rosane Freitas; Nakajima, Makoto; Sugino, RyosukeIn recent years, different methods of fermentation have emerged for coffee, with the intention of adding complexity to its flavor. To be able to clearly identify the information from sensory analysis, tools capable of detecting small differences are needed. One such tool is multiple factor analysis (MFA). Thus, the objective of this experiment was to evaluate the effects of fermentation time and storage on the quality of sensory attributes using MFA. The coffee (Coffea arabica L.) samples collected for the study were from the Serra da Mantiqueira region – Brazil. In the present study, two natural coffee fermentation methods were evaluated, one using natural coffee microbiota (NF) and the other using a starter culture (Y), along with different times of anaerobic fermentation (0, 24, 48, 72, and 96h), followed by the pulping of the samples without the use of water. Sensory analysis of fermented coffee samples was performed immediately after drying and after six months of storage in permeable packaging in a refrigerated environment. Thus, the experiment was conducted in an intirely randomized design with a 2 x 5 x 2 factorial scheme (2 fermentation treatments; 5 fermentation times; 2 storage times). The highest scores and the attributes described in higher quality coffees, such as sweetness, acidity, and aftertaste, were attributed to coffees fermented for 96 hours. Results indicated that inoculation of the yeast Saccharomyces cerevisiae CCMA 0543 was responsible for maintaining the sensory qualities of the coffee fermented for 96 hours after 6 months of storage.Item Case study of modeling covariance between external factors and sensory perception of coffee(Universidade Federal de Lavras, 2023-08-18) Resende, Mariana; Borém, Flávio Meira; Cirillo, Marcelo ÂngeloAnalysis and inference of sensory perceptions in coffee beverages are complex due to numerous random causes intrinsic to productivity, preparation, and especially consumer and/or taster subjectivity. In this context, latent variables often composed of a combination of other observed variables are discarded from conventional analyses. Following this argument, this study aimed to propose a model of structural equations applied to a database, geographical indication of coffees in Serra da Mantiqueira, with a methodological contribution characterized by inclusion of a treatment effect, contemplated by different altitudes at which coffees were produced. From the methodology used, a covariance structure was estimated, and used in another statistical methodology to discriminate the effects. It is concluded that the proposed model proved to be advantageous for allowing the analysis of the relationship of latent variables, production and environmental variations, which are not considered in a sensorial analysis, and showed that, in fact, they influence the sensorial perception, for the coffees produced in the Serra da Mantiqueira region. The correlation structure generated from the covariance matrix adjusted by the model resulted in estimates that could be used in other statistical methodologies more appropriate to discriminate the effects, exemplifying the use of principal components.Item Evaluation of the behavior of coffee stored in cooled and natural environments(Universidade Federal de Lavras, 2023-01-11) Andrade, Ednilton Tavares de; Rezende, Renan Pereira; Borém, Flávio Meira; Rosa, Sttela Dellyzete Veiga Franco da; Rios, Paula de Almeida; Oliveira, Filipe da Silva deThe market value of coffee is strongly influenced by loss of quality, which makes storage one of the main steps in the entire production chain. The finite element method (FEM) and computational fluid dynamics (CFD) are numerical and computational techniques that facilitate the simulation of agricultural product storage systems. Computational modeling satisfactorily represents real experimentation, simplifies decision-making, and reduces costs. This study aimed to analyze mocha coffee storage for 6 months in a cooled environment with temperatures between 15 and 18 °C and in a natural environment. The water content, bulk density, specific heat, thermal conductivity, and thermal diffusivity were determined and colorimetry and sensory analysis were applied to compare initial and final samples of the product after storage. It was found that the water content and specific heat were the only properties that presented significant changes. Through sensory analysis, it was observed that the quality of the coffee was the same for both systems. A computational model was developed to simulate the heat transfer process during storage. The comparison of the simulation results with the experimental results for the temperature distribution in the grain mass showed overall mean relative errors of 2.34% for the natural environment and 5.74% for the cooled environment.Item A mixed model applied to joint analysis in experiments with coffee blends using the least squares method(Universidade Federal do Ceará, 2019) Paulino, Allana Lívia Beserra; Cirillo, Marcelo Angelo; Ribeiro, Diego Egídio; Borém, Flávio Meira; Matias, Gabriel CarvalhoThe aim of the present study was to propose a mixed model for a sensory analysis of four experiments with blends of different standards of quality, including the species Coffea Arabica L. and Coffea Canephora. Each experiment differed in the proportions used to formulate the blends and the concentrations used in preparing the beverages, these being 7% and 10% coffee powder for each 100 ml of water. The response variables under analysis were the sensory characteristics of the beverage found in an assessment made by a group of trained tasters, considering taste, bitterness and a final score. Each description followed a numerical rating scale of intensity that ranged from 0 to 10. The model was implemented using the least squares method; this led to the conclusion that including random parameters in the model, represented by the experiments, made it possible to compare the effect of each component simultaneously for each of the experiments.Item Monte Carlo simulation and importance sampling applied to sensory analysis validation of specialty coffees(Universidade Federal do Ceará, 2021) Ferreira, Haiany Aparecida; Liska, Gilberto Rodrigues; Cirillo, Marcelo Ângelo; Borém, Flávio Meira; Ribeiro, Diego Egídio; Cortez, Ricardo MiguelCoffee sensory analysis is usually made by a sensory panel, which is formed by trained tasters, following the recommendations of the Specialty Coffee Association of America. However, the preference for a coffee is commonly determined by experimentation with consumers, who typically have no special skills in terms of sensory characteristics. Therefore, this study aimed at applying an intensive computational method to study sensory notes given by an untrained sensory panel, considering the probability distributions of the class of extreme values. Four types of specialty coffees produced under different processes and in varied altitudes in the mountainous region of Mantiqueira, Minas Gerais, were considered. We concluded that the generalized Pareto distribution can be applied to sensory analysis to discriminate types of specialty coffees. Furthermore, the method of importance sampling by Monte Carlo simulation showed greater variability considering a probabilistic model adjusted to identify specialty coffees.Item Statistical procedure for the composition of a sensory panel of blends of coffee with different qualities using the distribution of the extremes of the highest scores(Editora da Universidade Estadual de Maringá - EDUEM, 2019) Cirillo, Marcelo Ângelo; Ramos, Mariana Figueira; Borém, Flávio Meira; Miranda, Felipe Mesquita de; Ribeiro, Diego Egídio; Menezes, Fortunato Silva deThe identification and interpretation of discrepant observations in sensory experiments are difficult to implement since the external effects are associated with the individual consumer. This fact becomes more relevant in experiments that involve blends, which scrutinize coffees with different qualities, varieties, origins, and forms of processing and preparation. This work proposes a statistical procedure that facilitates the identification of outliers while also evaluating the discriminatory powers of a sensory panel concerning the differentiation of pure blends and coffees. For this purpose, four experiments were performed that tested coffees with different qualities and varieties. The results suggest that the statistical procedure proposed in this work was effective for discriminating the blends relative to the pure coffees and that the effects of the concentrations and types of processing did not interfere with the statistical evaluations.Item Unsupervised classification of specialty coffees in homogeneous sensory attributes through machine learning(Editora UFLA, 2020) Ossani, Paulo César; Rossoni, Diogo Francisco; Cirillo, Marcelo Ângelo; Borém, Flávio MeiraBrazil is the largest exporter of coffee beans, 29% world exports, 15% this volume in specialty coffees. Thereby researches are done, so that identify different segments in the market, in order to direct the end consumer to a better quality product. New technologies are explored to meet an increasing demand for high quality coffees. Therefore, in this article has an objective to propose the use of machine learning techniques combined with projection pursuit in the construction of unsupervised classification models, in a sensory acceptance experiment, applied to four groups of trained and untrained consumers, in four classes of specialty coffees in which they were evaluated sensory characteristics: aroma, body coffee, sweetness and general note. For evaluating classifier performance, in the data with reduced dimension, all instances were used, and considering four groupings, the models were adjusted. The results obtained from the groupings formed were compared with pre-established classes to confirm the model. Success and error rates were obtained, considering the rate of false positives and false negatives, sensitivity and classification methods accuracy. It was concluded that, machine learning use in data with reduced dimensions is feasible, as it allows unsupervised classification of specialty coffees, produced at different altitudes and processes, considering the heterogeneity among consumers involved in sensory analysis, and the high homogeneity of sensory attributes among the analyzed classes, obtaining good hit rates in some classifiers.Item Mathematical modeling of dehydration resistance of pericarp tissues and endosperm in fruits of arabic coffee(Editora UFLA, 2020) Dias, Camila de Almeida; Andrade, Ednilton Tavares de; Lemos, Isabella Àvila; Borém, Flávio Meira; Westerich, Diogo Nogueira; Silva, Ana Claudia Almeida daCoffee represents an important source of income for producers and for the Brazilian economy, being the second product in the country’s agricultural exports. Unlike other agricultural products, freshly harvested coffee has a high fruit water content, approximately 60% (dry base). It is fundamental to optimize the drying process for cost reduction and quality maintenance, making it necessary to understand the interdependence relation of the tissues of the pericarp and the coffee endosperm during the dehydration of the fruit. The objective of this work was to elaborate a drying model for the constituent parts of coffee fruits evaluating the resistance of each of the pericarp tissues and endosperm. The experiment was set up in a 4x6 factorial scheme (4 relative humidity of the drying air and natural, pulped natural coffee, pericarp tissues and endosperm: 1 - natural coffee and 2 – pulped natural coffee, 3 - exocarp + a portion of mesocarp, 4 - mesocarp, 5 - endocarp, 6 - endosperm]) in a completely randomized design with four replicates. The results were analyzed through analysis of variance and regression, using the statistical software STATISTICA 5.0®. The resistance to water outflow, regardless of the processing or the fruit part of the coffee, is greater when the coffee is dried with the lowest relative humidity. The natural coffee was the treatment that presented greater resistance, while the lower resistance was presented by the exocarp + a portion of mesocarp.Item Meteorological variables and sensorial quality of coffee in the Mantiqueira region of Minas Gerais(Editora UFLA, 2019-01) Borém, Flávio Meira; Luz, Marcos Paulo Santos; Sáfadi, Thelma; Volpato, Margarete Marin Lordelo; Alves, Helena Maria Ramos; Borém, Rosângela Alves Tristão; Maciel, Daniel AndradeThe objective in this study was to identify meteorological variables related to the sensorial quality of the coffees from Mantiqueira region in Minas Gerais. Meteorological conditions are strongly related to the coffee’s sensorial characteristics, however, there aren’t many studies quantifying this relation. Air temperature and rainfall data were collected and spatialized for regional analysis. These were associated to the 2007 through 2011 coffees’ beverage scores. The region was stratified according to relief characteristics. The bigger frequency of high scores occurred on the region’s central-south, where coffee cultivation is performed above 900 m altitude. For the in loco study, meteorological data and coffee samples were collected in selected pilot areas. Coffee crops were selected in three altitude ranges: below 1000 m, between 1000 and 1200 m, and over 1200 m. Above 1000 m the meteorological variable that presented the biggest variation was the air temperature. Above 1000 m the smallest thermal amplitude occurred, which provided superior quality coffees. The study demonstrates the importance of the meteorological variable characterization aiming to identify locations with greater vocation to the specialty coffees production.Item Physiological and sensorial quality of Arabica coffee subjected to different temperatures and drying airflows(Editora da Universidade Estadual de Maringá - EDUEM, 2017-04) Alves, Guilherme Euripedes; Borém, Flávio Meira; Isquierdo, Eder Pedroza; Siqueira, Valdiney Cambuy; Cirillo, Marcelo Ângelo; Pinto, Afonso Celso FerreiraThe objective of this study was to evaluate the correlation between a group of physiological variables (electrical conductivity, potassium leaching, and germination percentage) and a group of drying kinetics variables (drying time and drying rate) in addition to verifying the relation between drying kinetics variables and coffee quality as a function of processing type, temperature, and drying airflow. Coffee drying was conducted in a fixed-layer dryer at two temperatures and two airflows. After drying, an evaluation of the physiological and sensorial quality was conducted. Based on the results obtained, the following conclusions were drawn: coffee that is processed via a dry method is more sensitive to mechanical drying with heated air than coffee processed via a wet method, resulting in poor physiological performance; airflow does not interfere with the physiological quality of pulped and natural coffees; a temperature increase from 40 to 45°C resulted in a decrease in the physiological quality only for pulped coffee; and an increase in the drying rate as a result of an increase in the drying temperature to 40°C had a negative effect on the sensorial quality of pulped coffee.