.: Marcos Gestal :.     -----


Datos de la Publicación:

Marcos Gestal, Mari Paz Gómez, José Manuel Andrade, Julián Dorado, Esther Fernández, Darío Prada, Alejandro Pazos
Título: Selection of variables by genetic algorithms to classify apple beverages by artificial neural networks
Revista: Applied Artificial Intelligence
ISSN: 0883-9514
Volumen: 19(2)
Páginas: 181-198
Editorial: Taylor & Francis Inc
Fecha Publicación: Febrero 2005
Factor de Impacto: 0.629
doi: 10.1080/08839510590901921
Categorías WoS: Computer Science, Artificial Intelligence - Cuartil: Q3 - Tercil: T3 - Posición 56 de 79
Engineering, Electrical & Electronic - Cuartil: Q3 - Tercil: T2 - Posición 116 de 208
Citas ISI: 3
Citas Scopus: 3
Citas Google Scholar: 5


The importance of fruit beverages, and of apple juice in particular, in daily food habits makes juice authentication (in important issue in order to avoid fraudulent practices and to protect human health. Among the instrumental techniques available in analytical laboratories, infrared spectrometry (IR) is a fast and convenient technique to perform screening studies in order to assess the quantity of pure juice in commercial beverages. The information gathered from the IR analyses has some ''fuzzy" characteristics (random noise, unclear chemical assignment, etc.) and, therefore, advanced computation techniques (Artificial Neural Networks or ANNs) are needed to develop ad hoc classification models. Disappointingly, the large number of variables derived from IR spectrometry makes ANN\s require too much training lime. As a result, this work studies two different approaches to apply genetic algorithms as a suitable method to select a small subset of variables intended to otimize the development of the ANN models. Their performance will be compared among them and with several linear methods as well.