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Yong XU, Chao LI, Qianxu YANG, et al. Establishment of Predicting Model of Hydrogen Cyanide Contents in Flue-cured Tobacco Mainstream Smoke Based on BP Neural Network[J]. JOURNAL OF YUNNAN AGRICULTURAL UNIVERSITY（Natural Science）, 2018, 33(1): 72-78. DOI: 10.12101/j.issn.1004-390X(n).201701028
Citation: Yong XU, Chao LI, Qianxu YANG, et al. Establishment of Predicting Model of Hydrogen Cyanide Contents in Flue-cured Tobacco Mainstream Smoke Based on BP Neural Network[J]. JOURNAL OF YUNNAN AGRICULTURAL UNIVERSITY（Natural Science）, 2018, 33(1): 72-78. DOI: 10.12101/j.issn.1004-390X(n).201701028
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Establishment of Predicting Model of Hydrogen Cyanide Contents in Flue-cured Tobacco Mainstream Smoke Based on BP Neural Network

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    • Abstract
  • Purpose This study aimed to grasp the release of hydrogen cyanide in flue gas of mainstream tobacco and provide guidance for the adjustment of cigarette formula, finally reduce the harmfulness of cigarettes.
    Method The hydrogen cyanide (HCN) in mainstream smoke and the contents of 25 chemical components in 182 samples of cured tobacco leaves were determined. The BP neural network was applied to build the model of forecasting the HCN in mainstream smoke. The general chemical components-cut tobacco moisture, chlorine, malonic acid, volatile acid, potassium and total nitrogen were as the inputs of the network and the HCN wais as the outputs.
    Results The model was external validated by 28 samples, the average relative prediction error of HCN was 7.88%, and the relative prediction error of most samples within 10%.
    Conclusions This model had good prediction accuracy; it could be used to flue-cured tobacco widely.
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