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《Acta Tabacaria Sinica》 2010-06
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Establishment and application of principal component analysis-support vector regression model in cigarette mainstream smoke analysis

ZHANG Ping-quan,GONG Zhen-lin,DU Xiu-min,YU Jing,JIN Lan-fengQuality Management Department of Huaiyin Cigarette Factory,China Tobacco JiangsuIndustrial Ltd.Co.,Jiangsu 223002,China  
A new quantitative regression model was built in combination with extracts feature from sample cluster using principal component analysis(PCA).A nonlinear model was built using support vector regression(SVR) to predict total particulate matter,tar and nicotine content in cigarette smoke.Results showed that the RMSEP(root mean square error of prediction) of total particulate matter,tar and nicotine in smoke was 0.61,0.47 and 0.04 respectively,reduced by 30.73%,26.12% and 8.15% compared with SVM method,indicating that PCA-SVM method resulted in high prediction accuracy.
【CateGory Index】: TS411.2
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