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Prediction of fatty methyl esters and physical properties of soybean oil/biodiesel blends by near and mid-infrared spectra using data fusion strategy

Abstract

The data fusion strategy was compared to conventional sensor methodology to evaluate the refractive index, the relative density, and the total fatty acid methyl esters in soybean oil/biodiesel blend samples using near infrared and mid-infrared spectral data coupled with linear and non-linear regression methods. The refractive index and the relative density values were obtained through a refractometer and densimeter system. The fatty acid methyl ester values were acquired by gas chromatography using the standard method (EN-14103:2011). The fusion data approach was applied successfully and showed the lowest root standard error of prediction (RSEP), at least for refractive index and fatty acid methyl ester in relation the other strategies.

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Publication details

The article was accepted on 17 Jul 2017 and first published on 17 Jul 2017


Article type: Paper
DOI: 10.1039/C7AY01638G
Citation: Anal. Methods, 2017, Accepted Manuscript
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    Prediction of fatty methyl esters and physical properties of soybean oil/biodiesel blends by near and mid-infrared spectra using data fusion strategy

    A. S. Luna, I. C.A. Lima, C. A. Henriques, L. R.R. Araujo, W. F. C. D. Rocha and J. Verdan, Anal. Methods, 2017, Accepted Manuscript , DOI: 10.1039/C7AY01638G

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