Model established under a certain condition can be applied to the new samples, environmental conditions or instrument status through the model transfer. In the process of pilot-on-line water extraction of Flos Lonicerae Japonicae, the content of chlorogenic acid is measured with High Performance Liquid Chromatography (HPLC) as a reference method, and a NIR quantitative model of chlorogenic acid is established by partial least square regression (PLSR)。 In order to solve the problem of model’s failure to predict accurately the content of chlorogenic acid in the samples of Flos Lonicerae Japonicae from different sources, the KS algorithm is used to select the representative samples from samples to be transferred, orthogonal signal regression (OSR)algorithm is used to correct the NIR spectral background of the samples from different sources. And deeply discussing how the OSR worked in the model transfer from different sources. After the model transferred, the RSEP of transferred model predicting the new batch samples decreases from 14.91% to 7.11%, RPD rises from 2.95 to 5.36, indicating the obvious improvement of prediction accuracy. The results show that the model transfer method which combines the KS algorithm with OSR can diminish the spectral background variation between the samples of different sources effectively, because it not only reduces the accidental errors of the spectral background from the pharmaceutical raw materials from different sources, but also eliminates the system errors in the preparation process of pilot-on-line water extraction and the OSR algorithm. Based on this, it could correct model failure caused by different sample sources. This paper explains the application principle of OSR. Make NIR model to be transferred between the pilot samples which medicinal raw material come from different sources by spectral background and selecting the representative samples regression. Strengthen the model’s adjustment with the batch variations of medicinal raw material and improve the robustness of the NIR quantitative model. It will provide a method for the rapid and on-line detection of the active ingredient content of multi-sources samples during the process of pilot-on line water extraction, and promote the application of NIR quantitative model in the preparation process of TCM.