The formation of partial trimethylsilyl ethers of bile acid methyl esters, and their analysis by gas chromatography

1965 ◽  
Vol 97 (3) ◽  
pp. 579-588 ◽  
Author(s):  
T. Briggs ◽  
S.R. Lipsky
Keyword(s):  
Gas Chromatography ◽  
Bile Acid ◽  
Methyl Esters ◽  
Acid Methyl ◽  
Trimethylsilyl Ethers

Gas chromatography of dimethylalkylsilyl ether derivatives of bile acid methyl esters

1980 ◽  
Vol 190 (2) ◽  
pp. 339-345 ◽  
Author(s):  
Akemi Fukunaga ◽  
Yoshio Hatta ◽  
Masataka Ishibashi ◽  
Hiroshi Miyazaki
Keyword(s):  
Gas Chromatography ◽  
Bile Acid ◽  
Methyl Esters ◽  
Acid Methyl ◽  
Derivatives Of

p-Toluenesulfonic acid/methanol: Mild reagent for the preparation of bile acid methyl esters

Steroids ◽  
1981 ◽  
Vol 37 (2) ◽  
pp. 239-242 ◽  
Author(s):  
B. Dayal ◽  
J. Speck ◽  
E. Bagan ◽  
G.S. Tint ◽  
G. Salen
Keyword(s):  
Bile Acid ◽  
Methyl Esters ◽  
Acid Methyl ◽  
Toluenesulfonic Acid

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.

On the determination of the distillation curve of fatty acid methyl esters by gas chromatography

Fuel ◽  
2022 ◽  
Vol 314 ◽  
pp. 123143
Author(s):  
Rodica Niculescu ◽  
Mihaela Năstase ◽  
Adrian Clenci
Keyword(s):  
Gas Chromatography ◽  
Fatty Acid ◽  
Fatty Acid Methyl Esters ◽  
Methyl Esters ◽  
Distillation Curve ◽  
Acid Methyl
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