烟草科技
2016-03-29 星期二
Product Technology:2018 ,13:71-76
ZHANG Jianqiang, LI Jingjing, LIU Weijuan, LI Changyu, WU Shujiao, YANG Yanmei. Establishing a predictive model for fast online determination of relative density and refractive index of e-cigarette liquids using near-infrared spectroscopy[J]. 烟草科技, 2018 (13): 71-76.
ZHANG Jianqiang, LI Jingjing, LIU Weijuan, LI Changyu, WU Shujiao, YANG Yanmei. Establishing a predictive model for fast online determination of relative density and refractive index of e-cigarette liquids using near-infrared spectroscopy[J]. Tobacco Science & Technology, 2018 (13): 71-76.

Establishing a predictive model for fast online determination of relative density and refractive index of e-cigarette liquids using near-infrared spectroscopy
ZHANG Jianqiang1,2, LI Jingjing1, LIU Weijuan1,2, LI Changyu2, WU Shujiao2, YANG Yanmei2
1. Ruvian Technology Ltd., No. 1699 Haiyuan North Road, Kunming 650000, China;
2. Yunnan Reascend Tobacco Technology(Group) Co., Ltd., No. 1699 Haiyuan North Road, Kunming 650000, China
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 e-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.
关键词:    E-cigarette liquid    Relative density    Refractive index    NIR    PSO-SVR   
Establishing a predictive model for fast online determination of relative density and refractive index of e-cigarette liquids using near-infrared spectroscopy
ZHANG Jianqiang1,2, LI Jingjing1, LIU Weijuan1,2, LI Changyu2, WU Shujiao2, YANG Yanmei2
1. Ruvian Technology Ltd., No. 1699 Haiyuan North Road, Kunming 650000, China;
2. Yunnan Reascend Tobacco Technology(Group) Co., Ltd., No. 1699 Haiyuan North Road, Kunming 650000, China
Abstract:
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 e-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.
Key words:    E-cigarette liquid    Relative density    Refractive index    NIR    PSO-SVR   
收稿日期: 2018-09-21     修回日期: 2018-10-22
DOI: 10.16135/j.issn1002-0861.2018.0420
基金项目: This work was supported by China Postdoctoral Science Foundation (No. 2017M623322XB) and the Science and Technology Project of Yunnan Reascend Tobacco Technology (Group) Co., Ltd. (No. RS2017BH01).
通讯作者: LIU Weijuan     Email:Liuweijuan@reascend.com
作者简介:
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参考文献:
[1] Cobb N K, Abrams D B. E-cigarette or drug-delivery device? Regulating novel nicotine products[J]. New England Journal of Medicine, 2011, 365(3):193-195.
[2] Carroll Chapman S L, Wu L T. E-cigarette prevalence and correlates of use among adolescents versus adults:a review and comparison[J]. Journal of Psychiatric Research, 2014, 54:43-54.
[3] Scheffler S, Dieken H, Krischenowski O, et al. Evaluation of e-cigarette liquid vapor and mainstream cigarette smoke after direct exposure of primary human bronchial epithelial cells[J]. International Journal of Environmental Research and Public Health, 2015, 12(4):3915-3925.
[4] Nicolaï B M, Beullens K, Bobelyn E, et al. Nondestructive measurement of fruit and vegetable quality by means of NIR spectroscopy:a review[J]. Postharvest Biology and Technology, 2007, 46(2):99-118.
[5] Nicolaï B M, Defraeye T, de Ketelaere B, et al. Nondestructive measurement of fruit and vegetable quality[J]. Annual Review of Food Science and Technology, 2014, 5:285-312.
[6] Zhang J Q, Liu W J, Zhang H H, et al. Automatic classification of tobacco leaves based on near infrared spectroscopy and nonnegative least squares[J]. Journal of Near Infrared Spectroscopy, 2018, 26(2):101-105.
[7] LI Pengfei, WANG Jiahua, CAO Nanning, et al. Selection of variables for MLR in Vis/NIR spectroscopy based on BiPLS combined with GA[J]. Spectroscopy and Spectral Analysis, 2009, 29(10):2637-2461.
[8] Fang Y, Park J I, Jeong Y S, et al. Enhanced predictions of wood properties using hybrid models of PCR and PLS with high-dimensional NIR spectral data[J]. Annals of Operations Research, 2011, 190(1):3-15.
[9] Chen Q S, Zhao J W, Liu M H, et al. Determination of total polyphenols content in green tea using FT-NIR spectroscopy and different PLS algorithms[J]. Journal of Pharmaceutical and Biomedical Analysis, 2008, 46(3):568-573.
[10] Rambla F J, Garrigues S, de la Guardia M. PLS-NIR determination of total sugar, glucose, fructose and sucrose in aqueous solutions of fruit juices[J]. Analytica Chimica Acta, 1997, 344(1/2):41-53.
[11] WANG Shengpeng, WAN Xiaochun, LIN Maoxian, et al. Estimating the quality of tea leaf materials based on contents of moisture, total nitrogen and crude fiber by NIR-PLS techniques[J]. Journal of Tea Science, 2011, 31(1):66-71.
[12] Jiang H, Liu G H, Mei C L, et al. Measurement of process variables in solid-state fermentation of wheat straw using FT-NIR spectroscopy and synergy interval PLS algorithm[J]. Spectrochimica Acta Part A:Molecular and Biomolecular Spectroscopy, 2012, 97:277-283.
[13] Duan P, Xie K G, Guo T T, et al. Short-term load forecasting for electric power systems using the PSO-SVR and FCM clustering techniques[J]. Energies, 2011, 4(1):173-184.
[14] PENG Ling, NIU Ruiqing, ZHAO Yannan, et al. Prediction of landslide displacement based on KPCA and PSO-SVR[J]. Geomatics and Information Science of Wuhan University, 2013, 38(2):148-152, 161.
[15] Alderman S L, Song C, Moldoveanu S C, et al. Particle size distribution of e-cigarette aerosols and the relationship to Cambridge filter pad collection efficiency[J]. Beiträge zur Tabakforschung International, 2015, 26(4):183-190.
[16] Tipping M E, Bishop C M. Probabilistic principal component analysis[J]. Journal of the Royal Statistical Society, 1999, 61(3):611-622.
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