Application of gas-sensor array technology for detection and monitoring of growth of spoilage bacteria in milk: A model study

2006 ◽  
Vol 565 (1) ◽  
pp. 10-16 ◽  
Author(s):  
John Erik Haugen ◽  
Knut Rudi ◽  
Solveig Langsrud ◽  
Sylvia Bredholt
2013 ◽  
Vol 22 (4) ◽  
pp. 249-255 ◽  
Author(s):  
Young Jun Kim ◽  
Han Young Yu ◽  
In-Bok Baek ◽  
Chang-Geun Ahn ◽  
Bong Kuk Lee ◽  
...  

2011 ◽  
Vol 55-57 ◽  
pp. 1819-1823
Author(s):  
Yin Long Wang ◽  
Ke Cheng Lin ◽  
Xi Wu Wang ◽  
Zhi Guang Geng ◽  
Qi Gen Zhong

On the basis of the brief overview of principles of the gas detection system, this paper has analyzed the characteristics, structure and identification theory to explain the method of gas detection based on an artificial neural network. And it has analyzed and researched gas detection system based on neural network and thus solved the problems such as cross-sensitiveness in present gas sensor. The results show that the gas sensor array "cross-sensitive" issue can be effectively solved through the combination of the pattern recognition of artificial neural network and the gas sensor array technology, which accordingly realizes qualitative identification for different gases and has broad application prospects.


1991 ◽  
Vol 7 (Supple) ◽  
pp. 1565-1568 ◽  
Author(s):  
Yukio Hiranaka ◽  
Hiro Yamasaki

2021 ◽  
Author(s):  
Fajar IAIN Hardoyono ◽  
Kikin Windhani

This study aimed to identify four bioactive compounds in turmeric (Curcuma longa L.) using gas sensor array based on molecularly imprinted polymer-quartz crystal microbalance (MIP-QCM). Four QCM sensors coated with...


2008 ◽  
Vol 134 (2) ◽  
pp. 660-665 ◽  
Author(s):  
L. Francioso ◽  
A. Forleo ◽  
A.M. Taurino ◽  
P. Siciliano ◽  
L. Lorenzelli ◽  
...  

2018 ◽  
Vol 273 ◽  
pp. 1556-1563 ◽  
Author(s):  
Sangjun Park ◽  
Inug Yoon ◽  
Sungwoo Lee ◽  
Hyojung Kim ◽  
Ji-Won Seo ◽  
...  

2014 ◽  
Vol 494-495 ◽  
pp. 955-959 ◽  
Author(s):  
Wen Na Zhang ◽  
Guo Jun Qin ◽  
Niao Qing Hu

Data from sensor array are often arranged in three-dimension as sample × time × sensor. Traditional methods are mainly used for two-dimension data. When such methods are applied, some time-profile information will lost. To acquire the information of samples, sensors and times more exactly, parallel factor analysis (PARAFAC) is investigated to deal with three-way data array. Through the analysis and classification of three kinds of oil odor samples, the performance of PARAFAC in gas sensor array signal analysis is verified and validated.


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