[Near infrared spectroscopy synergy interval wavelength selection method using the LSSVM model]

The present paper proposes a wavelength selection algorithm based on nonlinear factors named Synergy interval least squares support vector machines (siLSSVM). siLSSVM combines the interval strategy of wavelength selection method with the idea of synergy interval and overcomes the disadvantages of the traditional wavelength selection methods, i. e. ignoring the nonlinear factors. Taking the near infrared spectrum data of apple sugar as performance verification object of this new algorithm, comparing new algorithm with siPLS, the model performance has been greatly improved. The root-mean-square error (RMSEP) in new algorithm has increased respectively by 37.43% and 47.88% under the model of PLS and LSSVM, with increases of 6.04% and 7.31% in the correlative coefficient (RP). The examples illustrate that siLSSVM can efficiently select the optimum wavelength interval for spectrum data with strong nonlinear factors. This algorithm greatly improves the prediction accuracy and robustness of the model, which provides a new prospect for near infrared spectral with nonlinear factors to select wavelength.