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Os métodos automáticos de classificação têm sido amplamente utilizados em inúmeras situações, nas quais o método boosting tem se destacado por utilizar um algoritmo de classificação que considera um conjunto de dados de treinamento e, a partir desse conjunto, constrói um classificador com versões reponderadas do conjunto de treinamento. Dada es...
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... regard to beverage preparation, a concentration of 7% w/v was maintained using filtered water ready for consumption, free of any contamination and without added sugar. With these specifications, four types of specialty coffees codified in the samples as A, B, C, and D were used, as described in Table 1. For each type of coffee, the following sensory characteristics were assessed in the acceptance test: aroma, body, hardness, and final score, in four sessions, with the participation of a volunteer group of consumers with basic knowledge in regard to sensory analysis of coffees and another group without basic knowledge. ...
Citations
... This is because it takes less time to remove the necessary level of moisture content for samples of low layer thickness, which results in reduced TSS loss. The present result is in agreement with the works of [44] who reported that there is an increase in total sugar and soluble sugar ...
Tunnel solar dryer is the recently used drying method for better quality and safety of parchment coffee. However, the higher variation of drying temperature and RH along the long tunnel solar dryer results in a heterogeneous environment in the tunnel, which could make parchment coffee dried at different times or with different moisture contents. This study is aimed at investigating the effect of solar tunnel dryer zones at different zones of the dryer, divided into three zones from the inlet to the exit side of the drier and drying layer thicknesses on the drying time, drying kinetics, physicochemical, sensory, and fungal growth loads of parchment coffee. Furthermore, seven mathematical models were evaluated to select the best-fitting model for a specific zone to predict drying time. Results showed that dryer zones significantly (p
... With the purpose of measuring hit rates regarding the discrimination of specialty coffees, with a sensory panel consisting of groups of trained and untrained tasters, Liska et al. (2015) conducted a study considering the classifiers of the discriminant analysis via a boosting algorithm, in which the training set consisted of 70% original samples and the remaining sample comprised the testing set. Using a comparative method, the authors concluded that the classifier generated by Fisher's discriminant analysis had a reasonable discriminatory power, revealing a high power of discrimination for trained tasters and a low power of discrimination for untrained tasters. ...
The results of sensory evaluations of coffees are associated with latent factors, such as the particular subjectivity of each individual. Based on the foregoing, assessing the quality of a sensory panel for product discrimination basically depends on the statistical methodology to be used in data analysis. Following this argument, this study aimed to evaluate the feasibility of the EM - Expectation Maximization algorithm in discriminating groups of individuals, characterized by the degree of experience and knowledge in sensory analysis of coffees of different varieties, produced in the Serra da Mantiqueira micro-region, with different processing and altitudes. The main advantage of this algorithm is the fast convergence, when the current solution approaches the optimal solution with high precision. The disadvantage is because it is a deterministic optimization technique, which can only achieve a local optimization depending on the initialization, i.e., initial values input in the iterative procedure. It can be concluded that estimates of the correlation matrices obtained by the EM algorithm showed that the final grade has a greater influence of sweetness, in addition to discriminating groups of consumers with different sensory perceptions and in situations where the number of individuals in each group is unknown, the EM algorithm was accurate in estimating the proportion of individuals belonging to each group, assuming that the correlations of sensory responses follow a bivariate normal distribution.
... In the work presented by Liska et al. (2015), it was used Fisher's conventional linear discriminant analysis (LDA) and the discriminant analysis via boosting algorithm (Adaboost) as a proposal for a classification rule to discriminate trained and untrained tasters. The authors concluded that the boosting method applied to the discriminant analysis show a higher sensibility rate in the trained panel. ...
Specialty coffees have a big importance in the economic scenario, and its sensory quality is appreciated by the productive sector and by the market. Researches have been constantly carried out in the search for better blends in order to add value and differentiate prices according to the product quality. To accomplish that, new methodologies must be explored, taking into consideration factors that might differentiate the particularities of each consumer and/or product. Thus, this article suggests the use of the machine learning technique in the construction of supervised classification and identification models. In a sensory evaluation test for consumer acceptance using four classes of specialty coffees, applied to four groups of trained and untrained consumers, features such as flavor, body, sweetness and general grade were evaluated. The use of machine learning is viable because it allows the classification and identification of specialty coffees produced in different altitudes and different processing methods.
... Freund and Schapire (1996) have initially proposed the name Boosting Algorithm AdaBoost, in the context of classification problems, and this has drawn attention to the machine learning community, as well as to the related statistical areas. Many versions of this algorithm have been confirmed as being promising in terms of predictive accuracy (Liska et al. 2015;de Menezes et al. 2017). Friedman, Hastie, and Tibshirani (2000) has also demonstrated that the AdaBoost algorithm can be interpreted as a gradient algorithm in the functional space, inspired by the numerical and statistical optimization. ...
When performing mixture experiments, we observe that maximum likelihood methods present problems related to the collinearity, small sample size, and over/under dispersion. In order to overcome these problems, this investigation proposes a model built in accordance with a machine learning approach. This approach will be called Boosted Simplex Regression, which has been evaluated both in terms of accuracy and precision for the odds ratio. The advantages of this new approach are illustrated in a mixture experiment, which has made us conclude that the model Boosted Simplex Regression has unveiled not only better fit quality but also more precise odds ratio confidence intervals.
... In this context, the use of Boosting methods for classification problems allows us both to discriminate small differences between samples and to differentiate the promising attributes to produce more accurate results in the classification activity. Hence, (LISKA,2015) 5 briefly describes a methodological study comparing conventional discriminant analysis and the Boosting approach with the hypothesis that the results from a sensorial analysis of specialty coffee, produced in the region of the Serra da Mantiqueira, presents few variations if applied to a group of trained consumers, capable of discriminating small differences between the samples not captured by the conventional discriminant analysis. However, for this study, we consider that the response variable is composed of only two categories; consequently, the method of classifying Bagging is evaluated. ...
... LISKA (2015) 5 has accomplished a study on sensory analysis, considering the same dataset used in this research. However, the main objective is to discriminate groups of trained and non-trained tasters. ...
Automatic classification methods have been developed in the area of Machine Learning to facilitate the categorization of data. Among the most successful methods are Boosting and Bagging. While Bagging works by combining fit classifiers into the bootstrap samples, Boosting works by sequentially applying a sorting algorithm to reweigh versions of the training dataset, giving more weight to the erroneously classified observations in the previous step. These classifiers are characterized by satisfactory results, low computational cost, and simplicity of implementation. Given these characteristics, there is an interest in verifying the performance of these automatic methods compared to the classical methods of classification in Statistics such as Linear and Quadratic Discriminant Analysis. To compare these techniques, we have used the classification error rates of the models to improve the confidence in the use of Boosting and Bagging methods in more complex classification problem. This study applies these techniques to real and simulated data that have been composed of more than two categories in the response variable. This investigation stimulates the implementation of Boosting and Bagging, by assigning an application in Sensory Analysis. We have concluded that the automatic methods have an optimal classification performance, showing lower error rates compared to the Linear and Quadratic Discriminant Analysis in the tested applications.
... That study is corroborated by using distributions of extreme values which consider the effect of discrepant observations. Liska et al. (2015) proved by means of the boosting method applied to sensory analysis, that untrained consumers do in fact have a low rate of success in differentiating the quality of speciality coffees. ...
The aim of the present study was to propose a mixed model for a sensory analysis of four experiments with blends of different standards of quality, including the species Coffea Arabica L. and Coffea Canephora. Each experiment differed in the proportions used to formulate the blends and the concentrations used in preparing the beverages, these being 7% and 10% coffee powder for each 100 ml of water. The response variables under analysis were the sensory characteristics of the beverage found in an assessment made by a group of trained tasters, considering taste, bitterness and a final score. Each description followed a numerical rating scale of intensity that ranged from 0 to 10. The model was implemented using the least squares method; this led to the conclusion that including random parameters in the model, represented by the experiments, made it possible to compare the effect of each component simultaneously for each of the experiments.
... There are several studies that have considered this concept. Liska et al. (2015) evaluated an experiment that tested four types of specialty coffees. The factors that were considered included sensory attributes such as aroma, body, sweetness and final score. ...
The identification and interpretation of discrepant observations in sensory experiments are difficult to implement since the external effects are associated with the individual consumer. This fact becomes more relevant in experiments that involve blends, which scrutinize coffees with different qualities, varieties, origins, and forms of processing and preparation. This work proposes a statistical procedure that facilitates the identification of outliers while also evaluating the discriminatory powers of a sensory panel concerning the differentiation of pure blends and coffees. For this purpose, four experiments were performed that tested coffees with different qualities and varieties. The results suggest that the statistical procedure proposed in this work was effective for discriminating the blends relative to the pure coffees and that the effects of the concentrations and types of processing did not interfere with the statistical evaluations.
... In this sense, many works have been developed to understand the complexity that exists in the production of specialty coffees with specific and unique attributes (Borém et al., 2016;Silveira et al., 2016;Carvalho et al., 2016;Fassio et al., 2016;Figueiredo et al., 2015;Silva et al., 2014;Sunarharum et al., 2014;Scholz et al., 2013;Bertrand et al., 2006;Avelino et al., 2005). A method that has excelled as effective in research with specialty coffees is the use of statistical tools such as Principal Component Analysis, the Method of Content Analysis, the Decision Tree by Hierarchical Cluster Analysis by CHAID method, Artificial Neural Networks, Discriminant Analysis by Boosting method, among others, all featuring the most secure and reliable information about the quality of the coffee drink (Ramos et al., 2016;Sobreira et al., 2015;Ribeiro et al., 2016;Liska et al., 2015;DonFrancesco et al., 2014;Link et al., 2014). As for the wine (Morlat & Bodin, 2006), the coffee is considered a product of terroir, i.e., its quality is directly related to soil and climate conditions where it is cultivated (Piccino et al., 2014;Silva et al., 2014) and the differentiation of cafes through this principle makes it possible to determine potential areas to produce specialty coffees and to characterize the type of coffee from these areas, exploring their potentialities (Silva et al., 2014). ...
The aim of this work was to evaluate the sensory profile of specialty coffees, natural and pulped, from the region of Matas de Minas in the State of Minas Gerais, Brazil and correlate the sensory scores with the chemical composition of the grains. Twenty samples of Arabica coffee were assessed (10 Natural and 10 Pulped), as the sensory profile (Cup of Excellence) and sucrose content, bioactive compounds and fatty acids of raw beans. The processed pulped coffees stood out as the final scores. The attributes sweetness, acidity and flavor were important for the distinction of the pulped coffees, while natural coffees the determining attributes were body and acidity. The bioactive compounds and sucrose showed positive and negative correlation with the sensory attributes, respectively. The acids C14:0, C18:2 and C18:3, were relevant to the sensory distinction of natural coffees. The acids C18:0 and C20:2 showed positive correlation, and acids C18:2 and C18:3, negative, with the sensory attributes of the pulped coffees. The specialty coffees of the region of Matas de Minas feature distinct sensory profiles and it is possible to correlate them with the chemical composition of the grains.
The sensory analysis of coffees assumes that a sensory panel is formed by tasters trained according to the recommendations of the American Specialty Coffee Association. However, the choice that routinely determines the preference of a coffee is made through experimentation with consumers, in which, for the most part, they have no specific ability in relation to sensory characteristics. Considering that untrained consumers or those with basic knowledge regarding the quality of specialty coffees have little ability to discriminate between different sensory attributes, it is reasonable to admit the highest score given by a taster. Given this fact, probabilistic studies considering appropriate probability distributions are necessary. To access the uncertainty inherent in the notes given by the tasters, resampling methods such as Monte Carlo’s can be considered and when there is no knowledge about the distribution of a given statistic, p-Bootstrap confidence intervals become a viable alternative. This text will bring considerations about the use of the non-parametric resampling method by Bootstrap with application in sensory analysis, using probability distributions related to the maximum scores of tasters and accessing the most frequent region (mode) through computational resampling methods.
