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Item Stability of the color of roasted coffees stored in different packaging(Editora UFLA, 2025-03-07) Silva, Laís de Oliveira; Borém, Flávio Meira; Heinerici, Gabriel Carvalhaes; Cirillo, Marcelo Ângelo; Alves, Ana Paula de Carvalho; Haeberlin, Luana; Santos, Cláudia Mendes dosThe aim of this study was to evaluate the color behavior (L* and Ag) and the stability of melanoidin molecules in roasted coffees with different roasting levels, types of packaging and storage conditions. Two roasting intensities were performed (medium roast and dark roast), following the SCA protocol for specialty coffees. The samples were stored in two types of packaging (permeable packaging and hermetic packaging with valve) and evaluated in eight storage times (1, 24, 48, 72, 240, 480, 1080 and 1440 hours) after roasting. The packaging interfered in the preservation of both the color of the roasted coffee and the melanoidin content over time. The permeable packaging allowed greater color loss, a difference also noted when evaluating the L*. For medium roast coffees stored in hermetic packaging, the maximum storage time should be up to 200 hours, while for dark roast beans when stored in hermetic packaging, the storage time was up to 480 hours. Greater stability of melanoidins was observed for coffees stored in hermetic packaging, with averages of 0.37 g.100 mL-1, while for coffees stored in permeable packaging the average was 0.34 g.100 mL-1, probably due to the oxidation of melanoidin molecules that reacted with oxygen and relative humidity of the ambient air. It is concluded that hermetic packaging presented a greater capacity for preserving the initial characteristics over the storage time, while permeable packaging allowed the loss of color of the roasted beans due to storage and possible modifications of the melanoidin molecules.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 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 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.