Selection of the Effective Characteristic Spectra Based on the Chemical Structure and Its Application in Rapid Analysis of Ethanol Content in Gasoline

Abstract

Near-infrared (NIR) spectroscopy analysis is one of the most rapid detection methods for determining ethanol content in gasoline. Wavelength selection is a key step in the multivariate calibration analysis of NIR spectroscopy. To improve detection accuracy of ethanol content in gasoline and provide a simpler interpretation, we established NIR spectroscopy, a rapid analysis method based on the effective characteristic spectra. Five effective characteristic spectral bands were used according to the molecular structure of ethanol, followed by the development of four modeling schemes. The four modeling schemes spectra, NIR full spectra, and variable importance projection (VIP) spectra were used for modeling and analysis. The model was established based on the effective characteristic spectra without the interference spectra of aromatic hydrocarbons, achieving the best model performance. In addition, the model was further evaluated by internal cross-validation and external validation. The model’s evaluation parameters were as follows: the root mean square error of cross-validation (RMSECV) was 0.6193, the correlation coefficient of internal cross-validation ( R CV 2 ) was 0.9995, the root mean square error of prediction (RMSEP) was 0.5572, and the correlation coefficient of external prediction validation ( R P 2 ) was 0.9995. The effective characteristic spectra model had smaller RMSEP and RMSECV values, and larger R CV 2 and R P 2 values compared to the full spectra and VIP spectra models. In conclusion, the effective characteristic spectra model had the highest accuracy and could provide rapid analysis of the ethanol content in gasoline.

Application of Bronze Sculpture Designs and Patterns in Painting

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Simultaneous Rapid Detection of Multiple Physicochemical Properties of Jet Fuel Using Near-Infrared Spectroscopy

“…By combining chemometric algorithms, a rapid detection model can be established between the spectra and target properties, thus enabling the qualitative or quantitative analysis of samples. − NIR spectroscopy is highly efficient, rapid, and nondestructive and can simultaneously determine multiple components. It has been widely applied in various industries, such as chemical engineering, agriculture, and medicine. − In recent years, researchers have applied NIR spectroscopy to the characterization and quality detection of jet fuels. By utilizing NIR spectroscopy in conjunction with chemometrics, multivariate regression models have been constructed to rapidly detect the physicochemical properties of jet fuels. − Xiao et al developed a rapid analysis model for the total acid value of jet fuel based on NIR spectroscopy using a backpropagation neural network (BP-ANN) and partial least-squares (PLS) regression methods.…”

“…It has been widely applied in various industries, such as chemical engineering, agriculture, and medicine. 5 − 11 In recent years, researchers have applied NIR spectroscopy to the characterization and quality detection of jet fuels. By utilizing NIR spectroscopy in conjunction with chemometrics, multivariate regression models have been constructed to rapidly detect the physicochemical properties of jet fuels.…”