Intensive Computational Method Applied for Assessing Specialty Coffees by Trained and Untrained Consumers (original) (raw)
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Coffee 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.
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Sensory science uses the human senses as instruments of measurement. Sensory analysis makes it possible to study organoleptic properties of products using a panel of assessors. say that a good sensory panel should provide results that are accurate, discriminating and precise. Thus, in a successful analysis, it is key to have a set of robust tools for monitoring individual assessor's performances as well as the performance of the panel as a whole.
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The objective of this work was to evaluate the performance of coffee tasters in five annual editions of Minas Gerais Coffee Quality Contest. The repeatability coefficients of the tasters' scores for sensory attributes were estimated, as well as the minimum numbers of tasters required for consistent sensory results, and the groups of tasters by (dis)similarity of sensory scores. For the repeatability analysis, the treatments (coffees) were tested with the repetitions, constituted by the tasters. The repeatability coefficients were estimated using the analysis of variance, principal component and structural analysis methods. The minimum number of tasters was obtained based on pre-established determination coefficients. Euclidean distance matrices between tasters were determined, which were used as a measure of dissimilarity for cluster analysis by the Tocher optimization method. The tasters' performance in five annual editions of Minas Gerais Coffee Quality Contest is reliable using COE or SCA sensory analysis protocols. Although not fully calibrated, most tasters are grouped with similar cupping results. Unless efficient calibration prior to the contest is adopted, the number of tasters to be used in the next contest editions can not be drastically and randomly reduced, since the estimated minimum number varied over the years. Calibration activities are suggested to improve two main aspects of the Minas Gerais Coffee Quality Contest: distinguishing the best coffees and trainning tasters.
Food Science and Technology, 2021
Coffee is one of the most popular beverages in the world, and changes in production, processing, trading, appreciation, and the culture of consumers are noticeable (Guimarães et al., 2019). Specialty and high-quality coffees are gaining space in the market, meeting the demands of consumers (Giacalone et al., 2019; Ufer et al., 2019). The worldwide consumption of specialty coffee is growing (with an increase of 1.5%), and this growth in the market is affected by new products, research, and specialized coffee shops (Guimarães et al., 2019). For these reasons, it is necessary to evaluate consumers' behavior and desires (Wang & Yu, 2016), detecting a large number of markets that can be explored with greater accuracy and quality. Market surveys are essential for understanding consumer intentions. The use of the internet (twitter, e-mail, among others) in the application of questionnaires using different qualitative sensory methods (such as completion task) has become very important to discover the motivations, perceptions, and attitudes of consumers about a product (Sass et al., 2020; Torres et al., 2020). However, there is not enough information about the specialty coffee market, especially about the characteristics and buying behavior of consumers (Guimarães et al., 2019). Several studies have shown that extrinsic factors, such as packaging, brand, information, emotion, and atmospheric, influence the sensory perception of food products (Spence, 2015;
2020
Brazil 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 cl...
Food Quality and Preference, 2002
Grading systems since their conception have aimed at facilitating commercialisation of food stuff world-wide. However, while many food products can have their quality assessed by analytical means, there are many foods that are sold according to their sensory quality and for which quality is not easily measured by conventional analytical techniques. Measuring sensory quality in some products has moved forward and utilises fully trained panels to set-up quality control systems and routine evaluations while others still rely on traditional commercial classifications and grading systems. The grading discussed here must be differentiated from grading using ''trained experts'' to evaluate food products according to legislated standards. There are specific cases in which the need to comply with national or international standards requires the development of specially trained tasting experts. Bisogni, [In D. E. Kramer, & J. Liston (Eds.), Seafood quality determination (p. 547). Amsterdam: Elsevier Science] illustrates quite well this specific scenario. The case of coffee, the most traded agricultural crop world-wide is examined here. In coffee a high diversity of classification systems is applied and the use of the ''expert cupper'' is the norm. There is not a unique and universal system applied world-wide for the quality control of green coffee. Tailor made procedures are selectively implemented by International, National, local bodies, trading institutions and private companies. Procedures are mostly geared to facilitate the trading of the commodity and sensory quality is in most cases described by ''cuppers'' or ''liquorers'' using personal opinion and tasting experience accumulated over the years. #
2015
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2011
Consumers' and experts' preferences and perceptions of the sensory attributes of products are very important for manufacturers in the food industry, in order to avoid market disappointment and improve food quality. Indeed, appropriate sensory analyses combined with proper statistical methods allow to segment market, obtain positioning of products (brands, organizations, etc.) and identify the market acceptability. This finally has a great impact upon food quality and industrial competitiveness. In this paper, we use CUB models to analyze sensory data coming from a survey on the Italian espresso.
Journal of Sensory Studies, 1996
Twenty judges performed a variety of chemosensory tasks in order to select the best scores to form a panel for coffee evaluation. An average of correct responses (P %}, one-way analysis of variance (ANOVA} and principal components analysis (PCA} were compared. f i e tests involved: ability to recognize the four basic tastes, identification and matching of odors, taste intensity evaluation and perception of small differences in taste. P% accounted for 71.1 7 f 4.34% and 10 of the judges had scores greater than the final average. ANOVA and PCA resulted in 2 different panels consisting of 9 and 12 judges, respectively. m e panel was composed by the nine panelists selected by the three methodr. m e other three panelists that were doubtful couM improve to the point of acceptance with aa'ditional training. fiese methods should be used simultaneously to have more security in the acceptance or rejection of panelists.