Revista Brasileira de Engenharia Agrícola e Ambiental
URI permanente para esta coleçãohttps://thoth.dti.ufv.br/handle/123456789/10362
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Item Inoculation of Saccharomyces cerevisiae with sugar cane juice as a starter culture in coffee (Coffea arabica) fermentation(Departamento de Engenharia Agrícola - UFCG, 2024-01) Ladino-Garzon, Wilmer L.; Barrios-Rodríguez, Yeison F.; Amorocho-Cruz, Claudia M.This study aims to evaluate the effect of sugarcane juice and the addition of commercial yeast Saccharomyces cerevisiae var. bayanus (≥ 1 × 1010 cfu/g) during the fermentation of coffee to the beverage’s sensory characteristics and the coffee bean’s chemical composition. A completely randomized experimental design with two replicates is carried out for four treatments, distributed as follows: i) water addition (0.78 kg), ii) sugar cane juice addition (0.78 kg), iii) sugar cane juice addition (0.78 kg) combined with yeast Oenoferm® Freddo (0.12 g) and iv) sugar cane juice addition (0.78 kg) combined with yeast Oenoferm® Color (0.12 g). After fermentation and drying, the samples were subjected to medium roasting and analyzed using infrared spectroscopy and sensory analysis according to the methodology of the Specialty Coffee Association. The implementation of organic additives directly affected the attributes and sensory notes, allowing coffee to be classified as a specialty coffee with a score above 80 points. Adding sugar cane juice or a combination of sugar cane juice and Saccharomyces cerevisiae showed promising results in improving coffee beverage quality. Additionally, chemometric analysis of the infrared spectrum showed that the chemical characteristics of roasted coffee were affected, which correlated with the sensory results. The addition of cane juice only (T2) and the Oenoferm® Freddo yeast strain (T3) presented the best sensory quality.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.