Please use this identifier to cite or link to this item: http://repositorio.unicamp.br/jspui/handle/REPOSIP/201077
Type: Artigo de periódico
Title: Quantification Of Animal Fat Biodiesel In Soybean Biodiesel And B20 Diesel Blends Using Near Infrared Spectroscopy And Synergy Interval Support Vector Regression.
Author: Filgueiras, Paulo Roberto
Alves, Júlio Cesar L
Poppi, Ronei Jesus
Abstract: In this work, multivariate calibration based on partial least squares (PLS) and support vector regression (SVR) using the whole spectrum and variable selection by synergy interval (siPLS and siSVR) were applied to NIR spectra for the determination of animal fat biodiesel content in soybean biodiesel and B20 diesel blends. For all models, prediction errors, bias test for systematic errors and permutation test for trends in the residuals were calculated. The siSVR produced significantly lower prediction errors compared to the full spectrum methods and siPLS, with a root mean squares error (RMSEP) of 0.18%(w/w) (concentration range: 0.00%-69.00%(w/w)) in the soybean biodiesel blend and 0.10%(w/w) in the B20 diesel (concentration range: 0.00%-13.80%(w/w)). Additionally, in the models for the determination of animal fat biodiesel in blends with soybean diesel, PLS and SVR showed evidence of systematic errors, and PLS/siPLS presented trends in residuals based on the permutation test. For the B20 diesel, PLS presented evidence of systematic errors, and siPLS presented trends in the residuals.
Subject: Animals
Biofuels
Calibration
Fats
Models, Theoretical
Soybeans
Spectroscopy, Near-infrared
Support Vector Machines
Animal Fat
Biodiesel
Partial Least Squares
Permutation Test
Support Vector Regression
Citation: Talanta. v. 119, p. 582-9, 2014-Feb.
Rights: fechado
Identifier DOI: 10.1016/j.talanta.2013.11.056
Address: http://www.ncbi.nlm.nih.gov/pubmed/24401458
Date Issue: 2014
Appears in Collections:Unicamp - Artigos e Outros Documentos

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