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Abstract: Near infrared (NIR) spectroscopy is a non-destructive (vibrational spectroscopy based) measurement technique for many multicomponent chemical systems, including products of petroleum (crude oil) refining and petrochemicals, food products (tea, fruits, e.g., apples, milk, wine, spirits, meat, bread, cheese, etc.), pharmaceuticals (drugs, tablets, bioreactor monitoring, etc.), and combustion products. In this paper we have compared the abilities of nine different multivariate classification methods: linear discriminant analysis (LDA), quadratic discriminant analysis (QDA), regularized discriminant analysis (RDA), soft independent modeling of class analogy (SIMCA), partial least squares (PLS) classification, K-nearest neighbor (KNN), support vector machines (SVM), probabilistic neural network (PNN), and multilayer perceptron (ANN-MLP) – for gasoline classification. Three sets of near infrared (NIR) spectra (450, 415, and 345 spectra) were used for classification of gasolines into 3, 6, and 3 classes, respectively, according to their source (refinery or process) and type. The 14,000–8000cm−1 NIR spectral region was chosen. In all cases NIR spectroscopy was found to be effective for gasoline classification purposes, when compared with nuclear magnetic resonance (NMR) spectroscopy or gas chromatography (GC). KNN, SVM, and PNN techniques for classification were found to be among the most effective ones. Artificial neural network (ANN-MLP) approach based on principal component analysis (PCA), which was believed to be efficient, has shown much worse results. We hope that the results obtained in this study will help both further chemometric (multivariate data analysis) investigations and investigations in the sphere of applied vibrational (infrared/IR, near-IR, and Raman) spectroscopy of sophisticated multicomponent systems. [Copyright &y& Elsevier]

Abstract: In this paper we have compared the abilities of two types of artificial neural networks (ANN): multilayer perceptron (MLP) and wavelet neural network (WNN) — for prediction of three gasoline properties (density, benzene content and ethanol content). Three sets of near infrared (NIR) spectra (285, 285 and 375 gasoline spectra) were used for calibration models building. Cross-validation errors and structures of optimized MLP and WNN were compared for each sample set. Four different transfer functions (Morlet wavelet and Gaussian derivative – for WNN; logistic and hyperbolic tangent – for MLP) were also compared. Wavelet neural network was found to be more effective and robust than multilayer perceptron. [Copyright &y& Elsevier]

Abstract: Six popular approaches of «NIR spectrum–property» calibration model building are compared in this work on the basis of a gasoline spectral data. These approaches are: multiple linear regression (MLR), principal component regression (PCR), linear partial least squares regression (PLS), polynomial partial least squares regression (Poly-PLS), spline partial least squares regression (Spline-PLS) and artificial neural networks (ANN). The best preprocessing technique is found for each method. Optimal calibration parameters (number of principal components, ANN structure, etc.) are also found. Accuracy, computational complexity and application simplicity of different methods are compared on an example of prediction of six important gasoline properties (density and fractional composition). Errors of calibration using different approaches are found. An advantage of neural network approach to solution of «NIR spectrum–gasoline property» problem is illustrated. An effective model for gasoline properties prediction based on NIR data is built. [Copyright &y& Elsevier]