Training sample selection is widely accepted as an important step in developing a near-infrared (NIR) spectro-scopic model. For industrial applications. the initial training dataset is usually selected empirically. This process is time-consuming, and updating the structure of the modeling dataset online is difficult. Considering the static structure of the modeling dataset, the performance of the established NIR model could be degraded in the online process. To cope with this issue, an active training sample selection and updating strategy is proposed in this work. The advantage of the proposed approach is that it can select suitable modeling samples automatically according to the process information. Moreover, it can adjust model coefficients in a timely manner and avoid arbitrary updating effectively. The effectiveness of the proposed method is validated by applying the method to an industrial gasoline blending process. (C) 2019 The Chemical Industry and Engineering Society of China, and Chemical Industry Press Co., Ltd. All rights reserved.