P1.9.16 Nonlinear Algorithms to Reduce the Dimension of Databases without Loss of Information
- 14th International Meeting on Chemical Sensors - IMCS 2012
2012-05-20 - 2012-05-23
- P1.9 Technology and Application
- J. Torrecilla, G. Matute, C. Calvo, C. Ceña, F. Rodríguez - Department of Chemical Engineering. Complutense University of Madrid (Spain)
- 1206 - 1208
In this work, self-organizing maps have been used in the reduction of the dimensionality of the database, extracting the essential information of the database, facilitating its handling and reducing the time needed by the sensor to give the measurement. This tool has been applied to a database composed of 220 1H NMR and 31P NMR spectra of 13 using edible vegetal oils (hazelnut, sunflower, corn, soybean, sesame, walnut, rapeseed, almond, palm, groundnut, safflower, coconut, and extra virgin olive oils). With this tool, the dimension of the databases decreases from 11 x 192 to 2 x 192. The loss of information was checked by comparing the statistical results shown here with others that can be found in literature. Here, using a low dimension database and without any other physicochemical feature, the statistical results have been slightly improved (the misclassification percentage decreases from 3 to less than 2.8%).