Determining degree of roasting in cocoa beans by artificial neural network (ANN)-based electronic nose system and gas chromatography/mass spectrometry (GC/MS)

2018 ◽  
Vol 98 (10) ◽  
pp. 3851-3859 ◽  
Author(s):  
Juzhong Tan ◽  
William L Kerr
Keyword(s):  
Neural Network ◽  
Mass Spectrometry ◽  
Artificial Neural Network ◽  
Gas Chromatography ◽  
Electronic Nose ◽  
Gas Chromatography Mass Spectrometry ◽  
Cocoa Beans ◽  
Artificial Neural ◽  
Artificial Neural Network Ann ◽  
Electronic Nose System

scholarly journals Upon the opportunity to apply ART2 Neural Network for clusterization of biodiesel fuels

2016 ◽  
Vol 3 (1) ◽  
pp. 19-25
Author(s):  
T. Petkov ◽  
Z. Mustafa ◽  
S. Sotirov ◽  
R. Milina ◽  
M. Moskovkina
Keyword(s):  
Neural Network ◽  
Mass Spectrometry ◽  
Artificial Neural Network ◽  
Gas Chromatography ◽  
Fatty Acid Methyl Esters ◽  
Methyl Esters ◽  
Gas Chromatography Mass Spectrometry ◽  
Acid Methyl ◽  
Chemometric Approach ◽  
Biodiesel Fuels

Abstract A chemometric approach using artificial neural network for clusterization of biodiesels was developed. It is based on artificial ART2 neural network. Gas chromatography (GC) and Gas Chromatography - mass spectrometry (GC-MS) were used for quantitative and qualitative analysis of biodiesels, produced from different feedstocks, and FAME (fatty acid methyl esters) profiles were determined. Totally 96 analytical results for 7 different classes of biofuel plants: sunflower, rapeseed, corn, soybean, palm, peanut, “unknown” were used as objects. The analysis of biodiesels showed the content of five major FAME (C16:0, C18:0, C18:1, C18:2, C18:3) and those components were used like inputs in the model. After training with 6 samples, for which the origin was known, ANN was verified and tested with ninety “unknown” samples. The present research demonstrated the successful application of neural network for recognition of biodiesels according to their feedstock which give information upon their properties and handling.

Portable electronic nose system with gas sensor array and artificial neural network

2000 ◽  
Vol 66 (1-3) ◽  
pp. 49-52 ◽  
Author(s):  
Hyung-Ki Hong ◽  
Chul Han Kwon ◽  
Seung-Ryeol Kim ◽  
Dong Hyun Yun ◽  
Kyuchung Lee ◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Gas Sensor ◽  
Electronic Nose ◽  
Sensor Array ◽  
Gas Sensor Array ◽  
Artificial Neural ◽  
Electronic Nose System
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