This article presents a study about the rapid characterization of petroleum naphtha properties based on near-infrared (NIR) spectroscopy. The major challenge of this problem is the low prediction accuracy and poor robustness of predictive models caused by significant noise and insufficient sample data. To address this challenge, a machine learning (ML) method and a correlation analysis (CA) method are applied, respectively. The ML approach uses wavelet transform to reduce noise and kernel partial least square (kPLS) algorithm to deal with the nonlinearities. The CA method utilizes the correlation relationship between petroleum naphtha samples and real-time NIR data to solve robustness problem. In order to exploit the advantages of both methods, a novel integration approach is then proposed, which systematically integrates the ML and correlation methods for both good accuracy and robustness. Application studies on NIR spectroscopy data from industry have been conducted. The results confirm the issues with only use of the ML or CA method and demonstrate the advantages of the proposed integrated approach and its potential in industrial applications.