The invention provides a method for establishing
multiple models of near
infrared spectrums. The method comprises the steps that collected near
infrared spectrums and corresponding detected constituent concentration data are divided into a
training set and a prediction set; a boosting method is used for sampling the
training set again, at the very start, the same sampling weight is given to all
wavelength points, and a certain number of
wavelength points are selected to establish a PLS submodel; a prediction spectrum is obtained through the product of the
score and the load of the PLS submodel; an index
loss function of the difference value of the prediction spectrum and a modeling subset spectrum is used for giving weights to all
wavelength points of a training subset; when the wavelength points are selected in the next time, and the larger the weight is, the larger the sampling probability of a sample is; the above steps are executed repeatedly, and a plurality of submodels are established; the weighted average of the prediction results of the models are used as the prediction concentrated value of a sample of the prediction set. According to the method, the submodels are established in the wavelength direction, the boosting method is used for conducting training continuously to establish the
multiple models at last, the prediction precision for quantitative analyzing the models is improved, and a novel quantitative analyzing method is provided for
multivariate calibration analyzing of the near
infrared spectrums.