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Journal of Analytical Methods in Chemistry
Volume 2018, Article ID 1795624, 10 pages
https://doi.org/10.1155/2018/1795624
Research Article

Rapid and Simultaneous Prediction of Eight Diesel Quality Parameters through ATR-FTIR Analysis

1Centro de Monitoramento e Pesquisa da Qualidade de Combustíveis, Biocombustíveis, Petróleo e Derivados (Cempeqc), São Paulo State University (UNESP), R. Prof. Francisco Degni 55 Quitandinha, 14800-900 Araraquara, SP, Brazil
2Instituto Federal de Educação, Ciência e Tecnologia de São Paulo (IFSP), Campus Matão, Rua Estéfano D’avassi, 625 Nova Cidade, 15991-502 Matão, SP, Brazil

Correspondence should be addressed to Maurilio Gustavo Nespeca; moc.liamg@ngoiliruam

Received 25 August 2017; Revised 13 November 2017; Accepted 28 November 2017; Published 5 February 2018

Academic Editor: Karoly Heberger

Copyright © 2018 Maurilio Gustavo Nespeca et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

Quality assessment of diesel fuel is highly necessary for society, but the costs and time spent are very high while using standard methods. Therefore, this study aimed to develop an analytical method capable of simultaneously determining eight diesel quality parameters (density; flash point; total sulfur content; distillation temperatures at 10% (T10), 50% (T50), and 85% (T85) recovery; cetane index; and biodiesel content) through attenuated total reflection Fourier transform infrared (ATR-FTIR) spectroscopy and the multivariate regression method, partial least square (PLS). For this purpose, the quality parameters of 409 samples were determined using standard methods, and their spectra were acquired in ranges of 4000–650 cm−1. The use of the multivariate filters, generalized least squares weighting (GLSW) and orthogonal signal correction (OSC), was evaluated to improve the signal-to-noise ratio of the models. Likewise, four variable selection approaches were tested: manual exclusion, forward interval PLS (FiPLS), backward interval PLS (BiPLS), and genetic algorithm (GA). The multivariate filters and variables selection algorithms generated more fitted and accurate PLS models. According to the validation, the FTIR/PLS models presented accuracy comparable to the reference methods and, therefore, the proposed method can be applied in the diesel routine monitoring to significantly reduce costs and analysis time.