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.
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callMore From: International Journal of Scientific Research in Science, Engineering and Technology
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
