Quantification of the contents in biojet fuel blends using near infrared spectroscopy and multivariate calibration

2017 ◽  
Vol 9 (31) ◽  
pp. 4616-4621 ◽  
Author(s):  
Luciana A. Terra ◽  
Paulo R. Filgueiras ◽  
Julio Cesar L. Alves ◽  
Ronei J. Poppi
Keyword(s):  
Infrared Spectroscopy ◽  
Near Infrared Spectroscopy ◽  
Near Infrared ◽  
Multivariate Calibration ◽  
Jet Fuel ◽  
Fuel Blends

A methodology was developed for quantification of blends of HEFA, farnesane and petroleum-derived jet fuel using near infrared spectroscopy and multivariate calibration.

Linear transformations of multivariate calibration models in near infrared spectroscopy: A comparative study

2017 ◽  
Vol 25 (4) ◽  
pp. 223-230 ◽  
Author(s):  
Joseph Dubrovkin
Keyword(s):  
Infrared Spectroscopy ◽  
Near Infrared Spectroscopy ◽  
Near Infrared ◽  
Multivariate Calibration ◽  
Cetane Number ◽  
Total Protein Content ◽  
Prediction Errors ◽  
Calibration Data ◽  
Linear Transformations ◽  
Calibration Models

It was shown that linear transformations are suitable for use in multivariate calibration in near infrared spectroscopy as data compression tools. Partial Least Squares calibration models were built using spectral data transformed by expansion in the series of classical orthogonal polynomials, Fourier and wavelet harmonics. These models allowed effective prediction of the cetane number of diesel fuels, Brix and pol parameters of syrup in sugar production and fat and total protein content in milk. Depending on the compression ratio, prediction errors were no larger than 30% of corresponding errors obtained by the use of the non-transformed models. Although selection of the most suitable transformation depends on the calibration data and on the cross-validation method, in many cases Fourier transform gave satisfactory results.

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