<abstract xmlns="http://www.w3.org/1999/xhtml"><p> 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.</p></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.

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

University “Prof. Dr. Assen Zlatarov” Bourgas, Bulgaria

Konstantin Preslavsky University of Shumen, Shumen, Bulgaria

Acta Scientifica Naturalis

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.

A chemometric approach using artificial neural network for clusterization of biodiesels was developed. It is based on artificial ART2 neural network. Gas...

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

Pubblicato online: 26 mar 2016

Pagine: 19 - 25

Ricevuto: 30 ott 2015

Accettato: 11 mar 2016

DOI: https://doi.org/10.1515/asn-2016-0003

Parole chiave
biodiesel fuel, fatty acid methyl esters, gas chromatography, vegetable oil, artificial neural networks

© 2016

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.

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

Pubblicato online: 26 mar 2016

Pagine: 19 - 25

Ricevuto: 30 ott 2015

Accettato: 11 mar 2016

DOI: https://doi.org/10.1515/asn-2016-0003

Parole chiavebiodiesel fuel, fatty acid methyl esters, gas chromatography, vegetable oil, artificial neural networks

© 2016

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.

Parole chiave
biodiesel fuel, fatty acid methyl esters, gas chromatography, vegetable oil, artificial neural networks