关键词: 红外光谱, 含氧化合物, 主成分回归, B P网络, 支持向量机回归

Abstract: A quantitative analysis model for the determination of the oxide concentration in a doublecomponent containing model oil is established based on the infrared spectroscopy data for cyclohexanone and cyclohexanol components in model oil, the regression of prominent component, and the regression of back propagation neural algorithm and support vector machine. The infrared spectroscopy data were compressed by principal component analysis and used as input information to develop models. The three quantitative models can predict the respective oxide contents, with the predictive ability of support vector machine model superior to the other two models, showing a good stability of support vector machine model. Comparing with traditional methods, the established model provides a simple, fast, nodamage, costeffective and green determination method.

Key words: Infrared spectroscopy, Oxygen compound, Prominent component regression, Back propagation network, Support vector machine for regression