期刊
CHEMOMETRICS AND INTELLIGENT LABORATORY SYSTEMS
卷 88, 期 2, 页码 183-188出版社
ELSEVIER SCIENCE BV
DOI: 10.1016/j.chemolab.2007.04.006
关键词
gasoline; principal component regression (PCR); linear partial least squares regression (PLS); polynomial partial least squares regression (Poly-PLS); spline partial least squares regression (Spline-PLS); artificial neural network (ANNI)
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. (c) 2007 Elsevier B.V. All rights reserved.
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