Strategy based on interval selection is widely used in the near-infrared spectroscopy analysis. Inspired by interval partial least-squares method (iPLS)

the present paper proposed a new wavelength method combining interval selection strategy with least-squares support vector machine (LSSVM).By overcoming the shortcomings of traditional interval selection methods whose predictive ability totally depend on the linear model

this new algorithm

named as iLSSVM (interval LSSVM)

can select the optimal informative interval more reasonably to significantly improve the model prediction accuracy with less modeling variables.Two real near-infrared datasets were applied to this new approach and the prediction performance was compared to the other interval selection methods.The experimental results demonstrated that the root mean square error of prediction (RMSEP) of this new method is 20% and 4% smaller than that of full-spectrum PLS modeling method respectively

and is 28% and 2% smaller than that of the traditional iPLS (interval partial least-squares) method respectively.