Spectral Reconstruction and Quantitative Analysis by B-Spline Transformations and Penalized Partial Least Squares Approach
Taking into account the near infrared spectra(NIR) on numerous predictor variables with serious collinearity and having nonlinear quantitative relationship with the chemical compositions,a novel nonlinear partial least squares(PLS) approach,termed as Spline-PPLS,was constructed by combining the penalized partial least squares(PPLS) regression with B-splines transformation.Firstly,the observed spectral predictors were considered as discrete observations of curves of the wavelength and were nonlinearly transformed using B-spline basis functions.The choice of the degree of the polynomial pieces and of the number of knots was performed using the cross-validation strategy.Then,the PPLS algorithm was performed on the high dimensional transformed data matrix to build the calibration model by imposing a penalty term to the optimization criterion of PLS.The roughness penalty term indeed controlled the curvature of the functions and its smoothing parameter could also be obtained by the cross-validation.Finally,the proposed Spline-PPLS approach was applied to the wheat NIR diffuse reflectance spectra reconstruction and quantitative analysis.The result indicates that the Spline-PPLS approach not only can yield high accuracy reconstructing spectrum,but also improves the model prediction accuracy in the case of nonlinear relationships.