【摘 要】
：
【目的】建立准确、环境友好的氢氰酸释放量预测方法,以减少检测过程中氢氰酸对人体的危害。【方法】测定了182份初烤烟叶样品的主流烟气氢氰酸释放量和25种烟叶化学成分,采
【基金项目】
：
云南中烟工业有限责任公司2015年重点项目（2015JC07）, 云南中烟工业有限责任公司2017年重点项目（2017JC04）, 云南省应用基础研究计划2016年青年项目（2017FD048）
论文部分内容阅读
【目的】建立准确、环境友好的氢氰酸释放量预测方法,以减少检测过程中氢氰酸对人体的危害。【方法】测定了182份初烤烟叶样品的主流烟气氢氰酸释放量和25种烟叶化学成分,采用BP神经网络,以卷烟常规化学成分烟丝水分、氯、丙二酸、挥发酸、钾、总氮作为神经网络的输入,主流烟气中氢氰酸作为输出,建立初烤烟叶主流烟气中氢氰酸释放量的预测模型。【结果】利用所建模型对28个样品进行外部验证,模型平均预测相对偏差为7.88%,大部分样品的预测相对偏差在10%以内。【结论】该预测模型预测精度良好,对于初烤烟叶具有广泛的适用性。
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