Evaluation of Coffee Roasting Degree by Using Electronic Nose and Artificial Neural Network for Off-line Quality Control

2012 ◽  
Vol 77 (9) ◽  
pp. C960-C965 ◽  
Author(s):  
Santina Romani ◽  
Chiara Cevoli ◽  
Angelo Fabbri ◽  
Laura Alessandrini ◽  
Marco Dalla Rosa
2022 ◽  
pp. 350-374
Author(s):  
Mudassir Ismail ◽  
Ahmed Abdul Majeed ◽  
Yousif Abdullatif Albastaki

Machine odor detection has developed into an important aspect of our lives with various applications of it. From detecting food spoilage to diagnosis of diseases, it has been developed and tested in various fields and industries for specific purposes. This project, artificial-neural-network-based electronic nose (ANNeNose), is a machine-learning-based e-nose system that has been developed for detection of various types of odors for a general purpose. The system can be trained on any odor using various e-nose sensor types. It uses artificial neural network as its machine learning algorithm along with an OMX-GR semiconductor gas sensor for collecting odor data. The system was trained and tested with five different types of odors collected through a standard data collection method and then purified, which in turn had a result varying from 93% to 100% accuracy.


2021 ◽  
Vol 12 (1) ◽  
pp. 36-44
Author(s):  
Dailyne Macasaet ◽  
◽  
Argel Bandala ◽  
Ana Antoniette Illahi ◽  
Elmer Dadios ◽  
...  

Author(s):  
Mudassir Ismail ◽  
Ahmed Abdul Majeed ◽  
Yousif Abdullatif Albastaki

Machine odor detection has developed into an important aspect of our lives with various applications of it. From detecting food spoilage to diagnosis of diseases, it has been developed and tested in various fields and industries for specific purposes. This project, artificial-neural-network-based electronic nose (ANNeNose), is a machine-learning-based e-nose system that has been developed for detection of various types of odors for a general purpose. The system can be trained on any odor using various e-nose sensor types. It uses artificial neural network as its machine learning algorithm along with an OMX-GR semiconductor gas sensor for collecting odor data. The system was trained and tested with five different types of odors collected through a standard data collection method and then purified, which in turn had a result varying from 93% to 100% accuracy.


2000 ◽  
Vol 66 (1-3) ◽  
pp. 49-52 ◽  
Author(s):  
Hyung-Ki Hong ◽  
Chul Han Kwon ◽  
Seung-Ryeol Kim ◽  
Dong Hyun Yun ◽  
Kyuchung Lee ◽  
...  

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