Coffee Science_v.15, 2020
URI permanente para esta coleçãohttps://thoth.dti.ufv.br/handle/123456789/12726
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Item Sensorial profile, content, and antioxidant activity in coffee beverages prepared by direct contact methods(Editora UFLA, 2020) Ormaza-Zapata, Angela María; Díaz-Arango, Félix Octavio; Rojano, Benjamín AlbertoDirect content coffee preparation methods may be used as alternative ways to obtain coffee beverages with varied cup profiles. In this investigation the antioxidant metabolites, antioxidant activity, and cup profiles were determined for coffee drinks prepared using five different direct contact methods. The method that registered greatest antioxidant retention was Ibrik, followed by French press, and Toddy. Antioxidant capacity was proportional to antioxidant component retention in the preparations made. It is recommended that coffee be prepared via the Ibrik, French press, and Toddy methods for high acceptance levels, as well as for retention of bioactive components with antioxidant properties and abilities.Item Microorganisms in coffee fermentation: A bibliometric and systematic literature network analysis related to agriculture and beverage quality (1965-2019)(Editora UFLA, 2020) Cruz-O’Byrne, Rosmery; Piraneque-Gambasica, Nelson; Aguirre-Forero, Sonia; Ramirez-Vergara, JoseThe activity of microorganisms in coffee fermentation has a great influence on the composition of the beans and their beverage quality. In the present study, a bibliometric and systematic literature network analysis is made to examine the growth in the literature and the flow of knowledge in the field of study. The bibliometric information was retrieved from the Scopus database, obtaining 55 articles between 1965 and 2019. Frequencies, co-authorship, and co-occurrence indicators were analyzed using Microsoft Excel and VOSviewer software. Our findings show that most of the articles have been published in the last decade and mainly on microbial diversity and starter cultures. Furthermore, it was possible to identify the most productive authors, the most influential works, the main journals where articles of the most productive authors and the most influential works have been published, the most productive affiliation countries, the most used keywords, the co-authorship taking authors and countries as the unit of analysis, the keyword co-occurrence, and the spatial distribution of studies with their research topics. This is the first bibliometric and systematic literature network analysis carried out on research articles on microorganisms in coffee fermentation related to agriculture and beverage quality, which becomes a tool for researchers in making decisions for the building and development of strategic plans for future research by understanding the trends and status of existing research in the field of study in accordance with the authors, works, affiliation countries, study topics, and patterns of international collaboration and within the academic community.Item Quality assessment of coffee beans through computer vision and machine learning algorithms(Editora UFLA, 2020) Santos, Fernando Ferreira Lima dos; Rosas, Jorge Tadeu Fim; Martins, Rodrigo Nogueira; Araújo, Guilherme de Moura; Viana, Lucas de Arruda; Gonçalves, Juliano de PaulaThe increasing market interest in coffee beverage, lead coffee growers around the world to adopt more efficient methods to select the best-quality coffee beans. Currently, coffee beans selection is carried out either manually, which is a costly and unreliable process, or using electronic sorting machines, which are often inefficient because some coffee beans defects, such as sour and immature beans, have similar spectral response patterns. In this sense, the present work aimed to analyze the importance of shape and color features for different machine learning techniques, such as Support Vector Machine (SVM), Deep Neural Network (DNN) and Random Forest (RF), to assess coffee beans’ defects. For this purpose, an algorithm written in Python language was used to extract shape and color features from coffee beans images. The dataset obtained was then used as input to the machine learning algorithms, developed using Python and R programing languages. The data reported in this study pointed to the importance of color descriptors for classifying coffee beans defects. Among the variables used, the components Gmean from RGB (Red, Green and Blue) color space and Vmean from HSV (Hue, Saturation and Value) color space were some of the most relevant features for the classification models. The results reported in this study indicate that all the classifier models presented similar performance. In addition, computer vision along with machine learning algorithms can be used to classify coffee beans with a very high accuracy (> 88%).Item Quality of Coffea canephora beverage as a function of genotype, processing method and grain size(Editora UFLA, 2020) Lima, Julião Soares de Souza; Silva, Samuel de Assis; Fonseca, Abel Souza da; Pajehu, Levi FragaAfter harvesting, the coffee beans tend to lose quality during fruit processing and grain storage, thus affecting the quality of the obtained beverage. The objective of this research was to evaluate the quality of the beverage obtained from conilon coffee (Coffea canephora) for seminal (S) and clonal (C) genotypes, two processing methods of the coffee cherries (natural and peeled), different sizes of coffee beans determined by sieves and two storing periods of 45 and 90 days. The coffee cherries were dried, natural (N) and peeled (P), on cement floor in greenhouse and classified through the 13, 14, 15, 16 and 17 sieves. After 45 days of storage, it is observed that the overall score (OS) of the beverage prepared from peeled clonal (PC) and natural seminal (NS) coffee beans increased with increasing bean size (sieves 15 and 16). The treatments PS13, PS14, PS15, PS16, PC15 and PC16 were significantly different, however, the overall score (OS) decreased after samples were stored for 90 days. It is concluded that after storing the coffee bean samples for 45 and 90 days, the OS decreased significantly for peeled seminal coffee (PS) sieves 13, 14, 15, and 16 and peeled clonal coffee (PC) sieves 15 and 16.Item Beverage quality of most cultivated Coffea canephora clones in the Western Amazon(Editora UFLA, 2020) Dalazen, Janderson Rodrigues; Rocha, Rodrigo Barros; Pereira, Lucas Louzada; Alves, Enrique Anastácio; Espindula, Marcelo Curitiba; Souza, Carolina Augusto deMost of the Western Amazon coffee production is made from growing unregistered clones, selected by the coffee growers themselves. The aim of this study is to evaluate the sensory profile and genetic diversity of the most cultivated Coffea canephora clones in the Western Amazon. Coffee samples at cherry stage of the clones 03, 05, 08, 25 and 66 were collected at eight municipalities in the main coffee growing zones, with altitudes ranging from 86 to 381 meters. Beverage quality was evaluated according to the Robusta Cupping Protocols and estimates of the genotype × environment interaction (GE) were made interpreting non-parametric and multivariate methods. The GE interaction was significant and the genetic component was also important to the expression of beverage quality (h2=82,23). The clones 25 and 05 have good attributes and mean score near 80 points. Sweetness was the sensory descriptor with the greatest impact on beverage quality of these two clones. Harshness was the descriptor that had the greatest negative impact on beverage quality of clone 66. The clones had complexities that differed and that were not necessarily associated with greater beverage quality. Despite the differences in their beverage attributes, these clones that are grown for their high productivity presented low genetic diversity of the beverage quality.Item Physicochemical, microbiological, and sensory analysis of fermented coffee from Sierra Nevada of Santa Marta, Colombia(Editora UFLA, 2020) Cruz-O’Byrne, Rosmery; Piraneque-Gambasica, Nelson; Aguirre-Forero, SoniaThe evaluation of the physicochemical (pH, degrees Brix, and temperature), microbiological (fungi, yeasts, and bacteria), and sensory characteristics (sensory attributes, score, and quality classification) of coffee wet fermentation in the Sierra Nevada of Santa Marta (SNSM), Colombia, was carried out to understand its dynamics and the correlation that exists between them. The fermentation process lasted 36 hours and samples were taken every six hours. The pH and degrees Brix gradually decreased in the fermentation time and showed a high dependence on each other. In 36 hours, the pH went from 5.37 to 3.96 and the degrees Brix from 6.53 to 4.30 °Bx. Fungi had the most abundant population throughout the fermentation process compared to bacteria and yeasts. The beverages obtained showed a high quality where the classification of excellent specialty coffees prevailed. The highest beverage quality was characterized by its sweetness, high acidity, floral notes flavored with lemongrass and cardamom, it was obtained at 18 hours of fermentation related to the highest fungi (6.92 log CFU.g-1) and yeast population (6.01 log CFU.g-1) and the lowest bacteria population (3.85 log CFU.g-1). Evaluating the physicochemical, microbiological, and sensory characteristics of fermented coffee in the SNSM is important in generating specific knowledge related to the fermentation process and coffee quality in the region and constitutes a tool for future research.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 Sensory Q-Grader evaluation of fermented arabica coffees by yeast (Saccharomyces cerevisiae) and lactic bacteria (Pediococcus acidilactici) cultures(Editora UFLA, 2020) Rodrigues, Sandy Dias; Coelho, Vinicius Serafim; Freitas, Valdeir Viana; Brioschi, Alessandra; Brioschi Júnior, Dério; Guarçoni, Rogério Carvalho; Pereira, Lucas Louzada; Eller, Monique Renon; Cardoso, Wilton SoaresThe objective of this study was to evaluate sensorially, by professional Q-Grader, the beverage coffee from fermentation natural and fermentation with the use of yeasts and lactic acid bacteria as starter cultures in wet coffee processing. The Arabica coffee was harvested at two different altitudes in Espírito Santo State. Both coffees went through 04 treatments: inoculated with starter cultures Saccharomyces cerevisiae (YML) or Pediococcus acidilactici CCT 1622 (LAF), natural fermentation (NF) (not inoculated) and Control, without fermentation (WF). The coffee was processed by just the wet process. After process and roasting, the sensorial analysis was performed to understand the impact of fermentation processing in the coffee quality, and was performed by 6 Q-Graders, following SCA protocol. The study evidenced that the use of natural fermentation or starter cultures during post-harvest coffee contributed to obtain a quality beverage with pleasurable sensorial characteristics, punctuated by the tasters in the overall score obtained and also by the high sensory scores in attributes such as fragrance, acidity, aftertaste and the different perceived aromas. This work demonstrates for coffee growers that fermentation technology is not intuitive but requires an understanding of the relationship of the microorganisms with the coffee and the environment. In addition to the other chemical aspects of roasting and brew coffee.Item Sensory quality prediction of coffee assessed by physicochemical parameters and Multivariate model(Editora UFLA, 2020) Domingues, Laricia Oliveira Cardoso; Garcia, Aline de Oliveira; Ferreira, Marcia Miguel Castro; Morgano, Marcelo AntônioBeverages from roasted coffee can be classified according to their sensory quality into Gourmet, Superior, Traditional, and not recommended for supply coffees. However, the sensory evaluation of coffee has been questioned as it can induce a subjective bias, since the assessors may be influenced by psychological, physiological, and/or emotional factors. Therefore, the aim of this study was to develop multivariate models for predicting the overall quality of Gourmet, Superior, and Traditional coffees, based on the physical and physicochemical parameters. One hundred and eight ground roasted coffee samples were evaluated for particle size, degree of roasting, histological identification, moisture, ash, aqueous extract, soluble solids (Brix), pH, and sensory profiling. All categories presented fine grinding. No significant differences were observed in the moisture content and soluble solids (Brix) of Gourmet, Superior, Traditional, not recommended for supply coffee samples. The Traditional and not recommended for supply presented higher levels of aqueous extract, ash, and pH. Light degree of roast and higher acidity values were observed with the increase in coffee quality grades. The results of the physical and physicochemical parameters and the principal component analysis allowed the separation of coffees into only two classes: high-quality (Gourmet and Superior) and low-quality (Traditional and not recommended). Furthermore, the one-class classification (OCC) method showed good sensitivity and was able to satisfactorily distinguish the Gourmet coffee samples from the other samples, in this way, this model can be used to corroborate but not replace the sensory analysis.Item Comparison of sensory attributes and chemical markers of the infrared spectrum between defective and non-defective Colombian coffee samples(Editora UFLA, 2020) Rodriguez, Yeison Fernando Barrios; Calderon, Karen Tatiana Salas; Hernández, Joel GirónDefects in coffee affect the sensory quality of finished drink. To avoid this, defective beans are usually removed after threshing, as, once the green beans have been roasted, it becomes difficult to identify the defects. Procedures have been developed to evaluate coffee samples using infrared spectroscopy to detect such defects. As such, this study evaluated infrared spectra and sensory attributes of 39 coffee samples in: commercial ground and instant coffees, medium and high roast quality coffees, and defects present in the coffee. The sensory analysis was performed by 10 judges, semi-trained by a Q-grader, and eleven attributes were assessed using a semi-structured hedonic scale. The spectra obtained from the coffee samples were processed by mean centering, normalization (probabilistic quotient normalization), area normalization, first derivative and second derivative, later followed by principal component analyses. The sensory results showed differences in the evaluated attributes, differentiating between the samples of high quality medium roasted coffee from the other samples. After processing IR spectra of the samples by area normalization, PCA results exhibited four different groups: a) medium, high roasted quality coffee, with broken and chipped defects; b) commercial ground coffee and defects of sour, insect damaged, and faded; c) black defects, and d) instant coffee. Using the chemical descriptors obtained from the infrared spectra, it was possible to separate between high quality, commercial and instant coffee.