The assessment of the quality of sugar using electronic tongue and machine learning algorithms

• Autor(a) principal: Sakata, Tiemi C.
• Data de Publicação: 2012
• Outros Autores: Faceli, Katti, Almeida, Tiago A., Júnior, Antonio Riul, Steluti, Wanessa M. D. M. F. [UNESP]
• Tipo de documento: Artigo de conferência
• Idioma: eng
• Título da fonte: Repositório Institucional da UNESP
• Texto Completo: http://dx.doi.org/10.1109/ICMLA.2012.98; http://hdl.handle.net/11449/73810
• topic: classification; electronic tongue; machine learning; sugar; K-nearest neighbors method; Machine learning methods; Physicochemical characteristics; Classification (of information); Electronic tongues; Learning systems; Sugars; Learning algorithms

The correct classification of sugar according to its physico-chemical characteristics directly influences the value of the product and its acceptance by the market. This study shows that using an electronic tongue system along with established techniques of supervised learning leads to the correct classification of sugar samples according to their qualities. In this paper, we offer two new real, public and non-encoded sugar datasets whose attributes were automatically collected using an electronic tongue, with and without pH controlling. Moreover, we compare the performance achieved by several established machine learning methods. Our experiments were diligently designed to ensure statistically sound results and they indicate that k-nearest neighbors method outperforms other evaluated classifiers and, hence, it can be used as a good baseline for further comparison. © 2012 IEEE.