  This paper presents the results of an automated volatile organic compound
(VOC) classification process implemented by embedding a machine learning
algorithm into an Arduino Uno board. An electronic nose prototype is
constructed to detect VOCs from three different fruits. The electronic nose is
constructed using an array of five tin dioxide (SnO2) gas sensors, an Arduino
Uno board used as a data acquisition section, as well as an intelligent
classification module by embedding an approach function which receives data
signals from the electronic nose. For the intelligent classification module, a
training algorithm is also implemented to create the base of a portable,
automated, fast-response, and economical electronic nose device. This solution
proposes a portable system to identify and classify VOCs without using a
personal computer (PC). Results show an acceptable precision for the embedded
approach in comparison with the performance of a toolbox used in a PC. This
constitutes an embedded solution able to recognize VOCs in a reliable way to
create application products for a wide variety of industries, which are able to
classify data acquired by an electronic nose, as VOCs. With this proposed and
implemented algorithm, a precision of 99% for classification was achieved into
the embedded solution.
