摘要
Relative density and refractive index are two fundamental physical properties of e?cigarette liquids to indicate their uniformity and batch stability.These parameters are mainly determined by a density meter and refractometer respectively,which is tedious and the analysis results are not readily available for massive measurements.A rapid determination of the two parameters is important for quality inspection and control of cigarette liquids,and a lot efforts have been devoted to establishing a predictive model for these parameters.In this study,a novel near-infrared spectroscopy(NIR)combined with particle swarm optimization-support vector regression(PSO-SVR)algorithm was applied to build a prediction model.The experimental results showed that comparing with the traditional partial least squares regression(PLSR)model and the principal component regression(PCR)model,the PSO?SVR model had superior prediction performance.
出处
《烟草科技》
EI
CAS
CSCD
北大核心
2018年第A01期71-76,共6页
Tobacco Science & Technology
基金
China Postdoctoral Science Foundation(No.2017M623322XB)
the Science and Technology Project of Yunnan Reascend Tobacco Technology(Group)Co.,Ltd.(No.RS2017BH01).
