Abstract：Aiming at the near infrared spectroscopy (NIR) with local effect sensitivity, numerous predictor variables with serious multicollinearity and having nonlinear quantitative relationship with the chemical compositions from the spectral data, a double-layer partial least squares (DNPLS) algorithm was constructed based on the error feedback-weighting correction. The model based on this proposed algorithm was divided into two parts: the outer part that embedded the nonlinear mapping between each pair of partial least square components into the regression framework of the partial least squares(PLS)method and the inner part that estimated the increment of weight vector by linear PLS method. Subsequently, to increase PLS components interpretative capability，the error-based weights updating procedure in the PLS input outer model was deduced and implemented in the DNPLS regression framework, Finally, the application to the corn sample water content modeling of the proposed DNPLS method was presented with a comparison to some other methods. The DNPLS method not only held a fine learning ability but also improved the prediction performance and steady capability.
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