Electronic nose using a bio-inspired neural network modeled on mammalian olfactory system for Chinese liquor classification

2019 ◽  
Vol 90 (2) ◽  
pp. 025001 ◽  
Author(s):  
Ying-Jie Liu ◽  
Ming Zeng ◽  
Qing-Hao Meng
Keyword(s):  
Neural Network ◽  
Electronic Nose ◽  
Olfactory System ◽  
Chinese Liquor

Detection of Mycobacterium tuberculosis (TB) in vitro and in situ using an electronic nose in combination with a neural network system

2004 ◽  
Vol 20 (3) ◽  
pp. 538-544 ◽  
Author(s):  
Alexandros K. Pavlou ◽  
Naresh Magan ◽  
Jeff Meecham Jones ◽  
Jonathan Brown ◽  
Paul Klatser ◽  
...  
Keyword(s):  
Neural Network ◽  
Mycobacterium Tuberculosis ◽  
Electronic Nose ◽  
Network System ◽  
Neural Network System

A Framework for an Artificial-Neural-Network-Based Electronic Nose

2022 ◽  
pp. 350-374
Author(s):  
Mudassir Ismail ◽  
Ahmed Abdul Majeed ◽  
Yousif Abdullatif Albastaki
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Artificial Neural Network ◽  
Electronic Nose ◽  
Learning Algorithm ◽  
General Purpose ◽  
Food Spoilage ◽  
Standard Data ◽  
Semiconductor Gas Sensor ◽  
Artificial Neural

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.

Feature Extraction of Citrus Juice During Storage for Electronic Nose Based on Cellular Neural Network

2020 ◽  
Vol 20 (7) ◽  
pp. 3803-3812 ◽  
Author(s):  
Huaisheng Cao ◽  
Pengfei Jia ◽  
Duo Xu ◽  
Yuanjing Jiang ◽  
Siqi Qiao
Keyword(s):  
Neural Network ◽  
Feature Extraction ◽  
Electronic Nose ◽  
Cellular Neural Network ◽  
Citrus Juice

An optimized deep convolutional neural network for dendrobium classification based on electronic nose

2020 ◽  
Vol 307 ◽  
pp. 111874 ◽  
Author(s):  
You Wang ◽  
Junwei Diao ◽  
Zhan Wang ◽  
Xianghao Zhan ◽  
Bixuan Zhang ◽  
...  
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Electronic Nose ◽  
Deep Convolutional Neural Network

scholarly journals Electronic Nose Technologies in Monitoring Black Tea Manufacturing Process

2020 ◽  
Vol 2020 ◽  
pp. 1-8
Author(s):  
Tharaga Sharmilan ◽  
Iresha Premarathne ◽  
Indika Wanniarachchi ◽  
Sandya Kumari ◽  
Dakshika Wanniarachchi
Keyword(s):  
Neural Network ◽  
Electronic Nose ◽  
Gas Sensors ◽  
Black Tea ◽  
Quality Parameters ◽  
Polyphenolic Compounds ◽  
Metal Oxide Semiconductor ◽  
Oxide Semiconductor ◽  
Optimum Level ◽  
Fermentation Stage

“Tea” is a beverage which has a unique taste and aroma. The conventional method of tea manufacturing involves several stages. These are plucking, withering, rolling, fermentation, and finally firing. The quality parameters of tea (color, taste, and aroma) are developed during the fermentation stage where polyphenolic compounds are oxidized when exposed to air. Thus, controlling the fermentation stage will result in more consistent production of quality tea. The level of fermentation is often detected by humans as “first” and “second” noses as two distinct smell peaks appear during fermentation. The detection of the “second” aroma peak at the optimum fermentation is less consistent when decided by humans. Thus, an electronic nose is introduced to find the optimum level of fermentation detecting the variation in the aroma level. In this review, it is found that the systems developed are capable of detecting variation of the aroma level using an array of metal oxide semiconductor (MOS) gas sensors using different statistical and neural network techniques (SVD, 2-NM, MDM, PCA, SVM, RBF, SOM, PNN, and Recurrent Elman) successfully.

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