Abstract
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.
Highlights
Volatile organic compound (VOC) classification is an important area in a wide range of industries like cosmetics, food, beverages and medical diagnosis, among others [1]
Analyzing some methods to build classifiers based on evolution rules as in [5] an electronic nose using a proposed design algorithm as the classifier module into the Arduino is considered to create such a solution
volatile organic compound (VOC), where the environment is sensed by the electronic portable system to recognize and classify VOCs, where the environment is sensed by the electronic nose nose conformed conformed by by aa sensors sensors array
Summary
Volatile organic compound (VOC) classification is an important area in a wide range of industries like cosmetics, food, beverages and medical diagnosis, among others [1]. Analyzing some methods to build classifiers based on evolution rules as in [5] an electronic nose using a proposed design algorithm as the classifier module into the Arduino is considered to create such a solution. This analysis led to consider a performance comparison among an ATMega 328. A novel hybrid active framework for evolving fuzzy system modeling is introduced. An evolving hybrid fuzzy based modeling approach is introduced in [14]. Reduced capabilities of an Arduino instead of a personal computer (PC)
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