Discrimination of the sensory quality of the <i>Coffea arabica</i> L. (cv. Yellow Bourbon) produced in different altitudes using decision trees obtained by the <scp>CHAID</scp> method

Ramos, Mariana F.; Ribeiro, Diego Egídio; Cirillo, Marcelo Ângelo; Borém, Flávio Meira

doi:10.1002/jsfa.7539

Cited by 19 publications

(15 citation statements)

References 15 publications

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“…Moreover, the quality of the Yellow Bourbon produced at different altitudes (below 1000 m, between 1000 and 1200 m and above 1200 m) was discriminated according to different scores. This discrimination was made through sensorial evaluations carried out by qualified, trained tasters such as the coffee judges special, introduced by RAMOS (2015), who has used decision trees to aggregate results that allow the researcher to pursue action on a large multivariate dataset. Analyzing the accuracy of the models based on the altitude ranges evaluated, the author has observed two results: (i) at altitudes above 1100 m exhibited precision values close to 80%; (ii) these values are considered acceptable with respect to correct the classification rates for the models attained with the CHAID method.…”

Section: Resultsmentioning

confidence: 99%

Machine Learning techniques in muliclass problems with application in sensorial analysis

Oliveira

Menezes

Cirillo

et al. 2019

Concurrency and Computation

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Summary 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.

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Section: Resultsmentioning

confidence: 99%

Machine Learning techniques in muliclass problems with application in sensorial analysis

Oliveira

Menezes

Cirillo

et al. 2019

Concurrency and Computation

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“…Por ello, el proceso de producción a nivel de fincas debe cumplir con los requerimientos mínimos exigidos tales como: variedad, altitud, cosecha y post cosecha (despulpado, fermentado, lavado, secado, almacenado y transporte) (Tolessa et al, 2019). Además, la calidad del café está asociada directamente a la altitud ya que pueden exhibir puntos de taza por encima de los 88 puntos como fue demostrado por Ramos et al (2016), por lo que la altitud es un factor clave a considerar. Si bien es cierto que todas las actividades del procesamiento del café son muy importantes, en Perú existen otras que aún no se han desarrollado adecuadamente por falta de innovación tecnológica como es el caso del secado.…”

Section: Universidad Nacional De Trujillounclassified

Effect of altitude on coffee (Coffea arabica L.) quality: comparison between mechanical and traditional drying

Guevara-Sánchez¹,

Bernales²,

Saavedra³

et al. 2019

Sci.agropecu

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“…Consumption and commercialization of coffee produced in Brazil holds a prominent position within the global economic scenario. In this context, numerous factors, such as origin and geographical designation (Ramos et al, 2016), forms of processing (Ribeiro et al, 2019;Ribeiro et al, 2016), specialty coffees and their blends (Paulino et al, 2019), and granulometric classification (Brighenti & Cirillo, 2019), have been researched as means to improve quality of coffee, aiming to expand the market and acquire new consumers.…”

Section: Introductionmentioning

confidence: 99%

An Index to Evaluate the Acceptance of Specialty Coffees in Consumer Groups

2020

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Numerous factors are related to the individual sensory perception of consumers, which makes it impossible to adapt a model that explains their behavior. In this context and given the scarcity of statistical indexes that evaluate preferences for specialty coffees, new statistical methods should be studied. To this end, our study aimed to create an index that measures the acceptance of specialty coffees. The index was built considering the fit of regression models as a function of principal component scores. Validation was done by significance tests, whose probabilities were obtained by bootstrapping, considering the main measures used in diagnosing outliers as weights, with application to real data from different consumer groups. The coffee varieties Acaia and Bourbon were discriminated in relation to altitude. In conclusion, the index was adequate for the analysis and characterization of specialty coffees grown at different altitudes.

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Discrimination of the sensory quality of the Coffea arabica L. (cv. Yellow Bourbon) produced in different altitudes using decision trees obtained by the CHAID method

Abstract: It was possible to discriminate cv. Yellow Bourbon coffee samples, the sensory evaluations of which resulted in scores of ≥88 points, which are associated with growing environments at altitudes of ≥1200 m. © 2015 Society of Chemical Industry.

Cited by 19 publications

References 15 publications

Machine Learning techniques in muliclass problems with application in sensorial analysis

Machine Learning techniques in muliclass problems with application in sensorial analysis

Effect of altitude on coffee (Coffea arabica L.) quality: comparison between mechanical and traditional drying

An Index to Evaluate the Acceptance of Specialty Coffees in Consumer Groups

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