【摘要】 针对烟草样品的近红外 (NIR)光谱与其总糖含量非线性相关的特点 ,提出了一种混合算法用于建立近红外光谱的非线性模型。该算法结合了偏最小二乘法 (PartialLeastSquare ,PLS)算法和人工神经网络(ArtificialNeuralNetwork ,ANN) ,把模型分成两个部分 :线性部分与非线性部分 ,并分别进行建模。与传统的多元校正算法PLS ,主成分回归 (PrincipleComponentRegression ,PCR) ,非线性PLS(NonlinearPLS ,NPLS)等相比 ,该混合算法所建的非线性参数模型的预测结果有明显的改善 ,从而为建立非线性模型提供了一种快速、准确的算法 ,可用于烟草样品总糖含量的定量分析。更多还原

【Abstract】 Near infrared spectroscopy (NIR) is an instrumental method applied for rapidly measuring the NIR spectra of pulverized tobacco samples and computing the chemical compositions from the spectral data. In the present paper, a mixed algorithm was employed for building the nonlinear model of NIR and the total sugar of tobacco samples. The mixed algorithm was combined with Partial Least Squares (PLS) method and Artificial Neural Network (ANN). The model based on the mixed algorithm was divided into two parts: linear part and nonlinear part, and the corresponding model of each part was built respectively. Compared with the classical multivariate calibration methods such as Principle Component Regression (PCR), PLS and nonlinear PLS (NPLS), the proposed procedure performed much better. The results showed that the mixed algorithm could be used for the quantitative analysis of the total sugar in tobacco samples.更多还原