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Título: Unsupervised classification of specialty coffees in Homogeneous sensory attributes through machine learning
Palavras-chave: Classification models
Data dimension reduction
Groupings identification
Projection pursuit
Cafés especiais - Análise sensorial
Aprendizado de máquina
Modelos de classificação
Redução dimensional de dados
Perseguição de projeção
Data do documento: Dez-2020
Editor: Universidade Federal de Lavras
Citação: OSSANI, P. C.; ROSSONI, D. F.; CIRILLO, M. Â.; BORÉM, F. M. Unsupervised classification of specialty coffees in Homogeneous sensory attributes through machine learning. Coffee Science, Lavras, v. 15, e151780, 2020. DOI: 10.25186/cs.v15i.1780.
Resumo: 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 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.
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