Estimation of gasoline properties by H-1 NMR spectroscopy with repeated double cross-validated partial least squares models

• Tipo: Artigo em Revista Científica Internacional
• Data: 2020

Autores

Leal, AL (Autor) Outra; Albuquerque, RM (Autor) Outra; Silva, AMS (Autor) Outra; Ribeiro, JC (Autor) Outra; Martins, FG (Autor) FEUP

Revista

Título: Journal of Chemometrics

Vol. 34

ISSN: 0886-9383

Editora: Wiley-Blackwell

Indexação

ISI Web of Knowledge - 0 Citações

Scopus - 1 Citação

Outras Informações

ID Authenticus: P-00R-MAJ

DOI: 10.1002/cem.3212

Abstract (EN): Commercial gasoline must satisfy several product specifications before trading. In the present work, repeated double cross validation using partial least squares regression was applied to create reliable prediction models for 13 physicochemical parameters (eg, density, vapour pressure, evaporate at 70 degrees C, evaporate at 100 degrees C, evaporate at 150 degrees C, final boiling point, research octane number, motor octane number, aromatic content, olefinic content, benzene content, oxygen content, and methyl tert-butyl ether content) of gasoline produced in Matosinhos' refinery. The input variables for the regression are the H-1 NMR spectral intensities of a total of 448 samples, which were recorded using a picoSpin NMR spectrometer operating at 80 MHz. The output variables are the corresponding property values, which were also measured according to ISO standard methods. A spectral feature elimination before multivariate analysis was done to remove noise and speed up the chemometric analysis. The optimum complexity of each model was achieved by repeated double cross-validation strategy, consisting of 100 repetitions of two nested cross-validation loops. Quantitative partial least squares yielded accurate predictions of 11 of 13 properties within the reproducibility of ISO standards. The methodology presented in this work has been proven effective in property estimation and enables a significant reduction in the total time of gasoline quality control.

Idioma: Inglês

Tipo (Avaliação Docente): Científica

Nº de páginas: 10