Authors:
Naren Mantilla-Ramirez
1
;
Homero Ortega-Boada
1
;
Milton Paja-Sarria
2
and
Alexander Sepúlveda-Sepúlveda
1
Affiliations:
1
Escuela de Ingenierías Eléctrica, Electrónica y de Telecomuniciones (E3T), Universidad Industrial de Santander, Bucaramanga, Colombia
;
2
Facultad de Ingenierías, Universidad Santiago de Cali, Cali, Colombia
Keyword(s):
Wood Identification, Timber Identification, Chemical Sensor Arrays, GMM-UBM, Gaussian Mixture Models, Universal Background Model.
Abstract:
Deforestation endangers some vulnerable wood species. Although there are effective timber species identification methods, they are typically expensive and time-consuming, they must be carried out by experts and they are not applicable to places far from main cities. In contrast, we propose to use electronic noses to identify timber species, e.g. during their transportation process, from the volatile compounds that timbers emanate. In the present work, it is proposed a method for timber species detection from their aromas. The measurements of the volatile compounds are made by an array of 16 chemical sensors, whose curves are the inputs to a pattern recognition system. Detection is performed by using Gaussian mixture modeling with Universal Background Model. In contrast to previous works, in this work, we apply a new approach to the problem of timer species detection; furthermore, the sample collection conditions are closer to those found in real situations; and, the number of samples
used is larger and more varied. We found an EER (equal error rate) of 24.18% for cedar verification and an EER of 33.62% for 4-timber species verification.
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