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Potential of electrospray ionization mass spectrometry (ESI-MS), using direct infusion, to quantify fatty acid methyl esters (FAME) in biodiesel

Abstract

Biodiesel is a very important biofuel obtained from vegetable oil or animal fat by a chemical reaction know as transesterification. Its product consists mainly of fatty acid methyl esters (FAME). In biodiesel quality control, limits and methodologies for each parameter are well established, and one of them is related to the total amount of FAME, which is measured by gas chromatography. However, the development of other analytical methods for such measurements can serve as alternative techniques. This work shows the use of mass spectrometry with direct infusion in electrospray ionization source (ESI-MS) as a promising technique to quantify the FAME present in biodiesel. Due to the chemical difference and the behaviour of the methyl esters, the ionizations occur at different scales, thus, to evaluate this behaviour, the concept of relative ionization efficiency (RIE) in the ESI source was used for quantification. Uncertainty estimations of intensities measurement by ESI-MS were also proposed. Four biodiesel samples (canola, coconut, crambe and soy) were synthesized, and their concentrations determined by the reference methods, GC-MS and GC-FID, and by the proposed method, ESI-MS. Then the efficiency of the method was evaluated comparing the methods with two approaches, one using the uncertainties and another considering a statistical analysis. In both situations, the methods could be considered as equivalent.

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Publication details

The article was received on 10 Mar 2017, accepted on 28 May 2017 and first published on 30 May 2017


Article type: Paper
DOI: 10.1039/C7AY00644F
Citation: Anal. Methods, 2017, Accepted Manuscript
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    Potential of electrospray ionization mass spectrometry (ESI-MS), using direct infusion, to quantify fatty acid methyl esters (FAME) in biodiesel

    R. V. Leal, G. F. Sarmanho, L. Leal, B. C. Garrido, L. J. Carvalho, E. C. P. Rego and P. R. Seidl, Anal. Methods, 2017, Accepted Manuscript , DOI: 10.1039/C7AY00644F

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