Prediction and Classification of Physical Properties by Near-Infrared Spectroscopy and Baseline Correction of Gas Chromatography Mass Spectrometry Data of Jet Fuels by Using Chemometric Algorithms
Year: 2012
Type: article
Abstract: Chemometric techniqueswere used to extract relevant information from near infrared (NIR) spectral data to accurately classify physical properties of complex fuel samples.Discrimination of fuel types and classification of flash point, freezing point, and boiling point of jet fuels were investigated.Optimal partial least squares discriminant analysis (oPLS-DA), fuzzy rule-building expert system (FuRES), and support vector machine (SVM) were used to build the calibration models between the NIR spectra and classes of physical property of jet fuels.oPLS-DA, FuRES, and SVM were compared with respect to prediction accuracy.The results indicated that combined with chemometric classifiers NIR spectroscopy would be a fast method to monitor the changes of jet fuel physical properties.Six widely used approaches of preprocessing NIR spectra were compared with respect to property prediction of jet fuels by NIR spectroscopy.These approaches included calculating the derivatives of spectra, multiplicative signal correction (MSC), standard normal variate (SNV) transformation, orthogonal signal correction (OSC), and two feature selection methods interval partial least squares (iPLS) and genetic algorithm
Author: Zhanfeng Xu
Institution: Ohio University
Language: English
Subfield: Analytical Chemistry
Field: Chemistry
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W2522897302