Predicting cetane number in diesel fuels using FTIR spectroscopy and PLS regression

[Display omitted] •Cetane number prediction of diesel fuels was performed using FTIR-PLS coupling.•FTIR fingerprints of 50 diesel samples were plotted.•The predictive abilities of the PLS model set up based on FTIR fingerprints using different data preprocessing was improved.•The FTIR-PLS approach w...

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Bibliographic Details
Published in:Vibrational spectroscopy 2020-11, Vol.111, p.103157, Article 103157
Main Authors: Barra, Issam, Kharbach, Mourad, Qannari, El Mostafa, Hanafi, Mohamed, Cherrah, Yahia, Bouklouze, Abdelaziz
Format: Article
Language:English
Subjects:
Analytical chemistry
Cetane number
Chemical Sciences
Cheminformatics
Chemometrics
Diesel
FTIR
Mathematics
PLS regression
Statistics
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Summary:[Display omitted] •Cetane number prediction of diesel fuels was performed using FTIR-PLS coupling.•FTIR fingerprints of 50 diesel samples were plotted.•The predictive abilities of the PLS model set up based on FTIR fingerprints using different data preprocessing was improved.•The FTIR-PLS approach was highly recommended for the estimation of diesel Cetane Number. Cetane number (CN) is an important property which indicates the ignition quality of fuels and especially diesel oil. The usual method for CN determination is a most involving and risky task that requires specific devices. In this paper, Partial Least Square Regression (PLSR) was successfully used for the prediction of diesel cetane number based on Fourier Transform Infrared Spectroscopy (FTIR). The proposed model was characterized by a high correlation coefficient between real and predicted CN values (R2 = 0.99), with small prediction error values (RMSEC = 0.28 and RMSEP = 0.42) compared to previously published models developed using spectroscopic techniques, namely NIR and Raman spectroscopy Thus, the proposed approach that uses the FTIR spectroscopy for cetane number determination can be highly recommended as a clean, environment friendly, rapid and reliable solution for the prediction of this important quality parameter of diesel fuels.
ISSN:0924-2031
1873-3697
DOI:10.1016/j.vibspec.2020.103157