The near-infrared models have been applied in huanglongbing detection and it has been proved to be feasible, but the present studies are limited to taking leaves as samples. The phloem of bark is a channel to transport pathogens and nutriment, it has been shown to play an important role in pathological initiation, progression and maintenance, so that we can detect huanglongbing in the early stages with the specific information of barks. In order to explore the feasibility of infrared spectroscopy based on the bark samples and analyze the influence of sampling position variability on near infrared models for huanglongbing, three kinds of sampling plan were designed in this paper: navel orange leaves, navel orange barks and composite samples (navel orange leaves and navel orange barks). Then, we established the prediction model of HLB (huanglongbing) with PLSR (partial least square regression) and PCR (principal component regression), when the normalization was turned out to be the optimal data preprocessing method. We found that the RMSEP (root mean squared error of prediction) are all at the level of 10-5: RMSEPL (RMSEP of leaves, 1.690 9×10-5)