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Wavelength selection is widely accepted as an important step in near-infrared (NIR) spectroscopic model development. In quantitative online applications, the robustness of the established NIR model is often jeopardized by instrument response changes, process condition variations or new sources of chemical variation. However, to the best of our knowledge, online updating of wavelength selection has not been considered in NIR modeling and property prediction. In this article, a new model-updating approach is proposed that can adjust to process changes by recursively selecting the NIR model structure in terms of wavelength. The advantage of the presented approach is that it can recursively adjust both wavelength selection and model coefficients according to real process variations. The performance of the method was tested on a spectroscopic data set from a refinery process. Compared with traditional PLS, locally weighted PLS, and several other updating strategies, the proposed method was found to achieve good accuracy in the prediction of diesel properties.

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