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URI permanente desta comunidadehttps://thoth.dti.ufv.br/handle/123456789/3352
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Resultados da Pesquisa
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 Estimation of percentage of impurities in coffee using a computer vision system(Departamento de Engenharia Agrícola - UFCG, 2022-01-14) Costa, Anderson G.; Silva, Eudócio R. O. da; Barros, Murilo M. de; Fagundes, Jonatthan A.The quality and price of coffee drinks can be affected by contamination with impurities during roasting and grinding. Methods that enable quality control of marketed products are important to meet the standards required by consumers and the industry. The purpose of this study was to estimate the percentage of impurities contained in coffee using textural and colorimetric descriptors obtained from digital images. Arabica coffee beans (Coffea arabica L.) at 100% purity were subjected to roasting and grinding processes, and the initially pure ground coffee was gradually contaminated with impurities. Digital images were collected from coffee samples with 0, 10, 30, 50, and 70% impurities. From the images, textural descriptors of the histograms (mean, standard deviation, entropy, uniformity, and third moment) and colorimetric descriptors (RGB color space and HSI color space) were obtained. The principal component regression (PCR) method was applied to the data group of textural and colorimetric descriptors for the development of linear models to estimate coffee impurities. The selected models for the textural descriptors data group and the colorimetric descriptors data group were composed of two and three principal components, respectively. The model from the colorimetric descriptors showed a greater capacity to estimate the percentage of impurities in coffee when compared to the model from the textural descriptors.