Recognition of Aromas from Tea Sources based on MQ3, MQ5, MQ7 Sensor Signal

Authors

  • Prof. Vinod Desai  Assistant Professor, Department of Computer Science and Engineering, Angadi Institute of Technology and Management Belagavi, Department of Computer Science and Engineering, Savagaon, Karnataka, India
  • Venkatesh Sonnad  Student, Department of Computer Science and Engineering, Angadi Institute of Technology and Management Belagavi, Dept. of Computer Science and Engineering, Savagaon, Karnataka, India
  • Sneha Patil  Student, Department of Computer Science and Engineering, Angadi Institute of Technology and Management Belagavi, Dept. of Computer Science and Engineering, Savagaon, Karnataka, India

DOI:

https://doi.org/10.32628/IJSRSET207460

Keywords:

MQ3, MQ5, MQ7, Morlet Function, Recognition Accuracy

Abstract

This study investigated the capacity of a deep neural network to distinguish tea types based on their aromas. The data set of aromas from tea leaves, which contained sensor responses measured with a gas–sensing system using a mass– sensitive chemical sensors namelyMQ3, MQ5, MQ7, was used to evaluate the recognition accuracy. To define the input vectors of the deep neural network in aroma recognition experiments, frequency analysis using a continuous wavelet transform, with the Morlet function as the mother wavelet, was used to extract features from the sensor signals of the data set. The deep neural network achieved a recognition accuracy of 100% for the three tea types: oolong, jasmine and pu’erh, and the base gas of dehumidified indoor air. Comparing the recognition accuracy of the deep neural network to that obtained from other pattern recognition methods, such as naive Bayes and random forests, the experimental results demonstrated the effectiveness of applying a deep neural network to this task.

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International Journal of Scientific Research in Science, Engineering and Technology

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Published

2020-08-30

Issue

Volume 7, Issue 4, July-August-2020

Section

Research Articles

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How to Cite

[1]
Prof. Vinod Desai, Venkatesh Sonnad, Sneha Patil "Recognition of Aromas from Tea Sources based on MQ3, MQ5, MQ7 Sensor Signal" International Journal of Scientific Research in Science, Engineering and Technology (IJSRSET), Print ISSN : 2395-1990, Online ISSN : 2394-4099, Volume 7, Issue 4, pp.259-264, July-August-2020. Available at doi : https://doi.org/10.32628/IJSRSET207460