湖北农业科学 ›› 2026, Vol. 65 ›› Issue (1): 90-96.doi: 10.14088/j.cnki.issn0439-8114.2026.01.016

• 园艺·特产 • 上一篇    下一篇

基于Lasso的打叶复烤烟叶常规化学成分预测模型的构建

齐永杰1, 徐文兵1, 张晓龙2, 赵东杰1, 路小改1, 毛春堂2, 农正斌2, 潘武宁1, 徐茂华1, 彭博1   

  1. 1.广西中烟工业有限责任公司,南宁 530001;
    2.云南五佳生物科技有限公司,昆明 650106
  • 收稿日期:2025-06-17 出版日期:2026-01-25 发布日期:2026-02-10
  • 通讯作者: 彭博(1983-),男,湖北云梦人,助理农艺师,硕士,主要从事烟叶复烤加工及质量评价研究工作,(电话)0771-3998675(电子信箱)20010330@qq.com。
  • 作者简介:齐永杰(1980-),男,河南柘城人,高级农艺师,硕士,主要从事烟叶生产及复烤加工研究工作,(电话)0771-3998675(电子信箱)174779065@qq.com。
  • 基金资助:
    广西中烟工业有限责任公司项目(2023450000340054)

Construction of a predictive model for routine chemical components in threshing and redrying of flue-cured tobacco leaves based on Lasso

QI Yong-jie1, XU Wen-bing1, ZHANG Xiao-long2, ZHAO Dong-jie1, LU Xiao-gai1, MAO Chun-tang2, NONG Zheng-bin2, PAN Wu-ning1, XU Mao-hua1, PENG Bo1   

  1. 1. China Tobacco Guangxi Industrial Co., Ltd., Nanning 530001, China;
    2. Yunnan Wooja Bio-tech Co., Ltd., Kunming 650106, China
  • Received:2025-06-17 Published:2026-01-25 Online:2026-02-10

摘要: 为构建打叶复烤烟叶常规化学成分预测模型,基于Lasso算法,采用双重特征选择机制,将复烤前烟叶化学成分与不同加工环节温度参数共同作为输入变量,揭示打叶复烤过程中“复烤前原料特性-工艺温度参数-复烤后质量”的联动机制。结果表明,氯离子含量预测模型效果最好,其次是氧化钾,再次是烟碱,总糖、还原糖和总氮含量模型效果较差。氯离子预测模型复烤前氯离子含量、一润热风温度和干燥二区温度特征指标回归系数分别为0.34、0.01、-0.01;氧化钾含量预测模型复烤前氧化钾含量、回潮一区温度特征指标回归系数分别为0.42、-0.14;烟碱含量预测模型一润热风温度、复烤前烟碱含量特征指标回归系数分别为0.32、0.26;总氮含量预测模型复烤前总糖含量、干燥二区温度、干燥三区温度、回潮一区温度特征指标回归系数分别为-0.12、0.08、-0.05、0.03;还原糖含量预测模型复烤前总糖含量、复烤前氯离子含量、干燥三区温度特征指标回归系数分别为1.95、 -0.90、0.60;总糖含量预测模型复烤前总糖含量、复烤前氯离子含量、干燥三区温度特征指标回归系数分别为2.20、-1.00、0.25。打叶复烤后烟叶氯离子、氧化钾和烟碱含量的预测模型效果较好,总氮、总糖和还原糖含量的预测模型效果较差,干燥区温度对复烤后烟叶常规化学成分具有重要影响。

关键词: 烟叶, 打叶复烤, Lasso, 预测模型, 常规化学成分

Abstract: To construct a prediction model for the routine chemical components of tobacco leaves during threshing and redrying, a dual feature selection mechanism based on the Lasso algorithm was employed. The chemical composition of tobacco leaves before redrying and temperature parameters from various processing stages were used as input variables to elucidate the linkage mechanism of "raw material characteristics before redrying-process temperature parameters-quality after redrying" during the threshing and redrying process. The results showed that the prediction model for chloride ion content performed the best, followed by the model for potassium oxide, and then the model for nicotine. The models for total sugar, reducing sugar, and total nitrogen content performed relatively poorly. In the chloride ion prediction model, the regression coefficients for chloride ion content before redrying, primary moistening hot air temperature, and drying zone 2 temperature were 0.34, 0.01, and -0.01, respectively. For the potassium oxide content prediction model, the regression coefficients for potassium oxide content before redrying and tempering zone 1 temperature were 0.42 and -0.14, respectively. In the nicotine content prediction model, the regression coefficients for primary moistening hot air temperature and nicotine content before redrying were 0.32 and 0.26, respectively. For the total nitrogen content prediction model, the regression coefficients for total sugar content before redrying, drying zone 2 temperature, drying zone 3 temperature, and tempering zone 1 temperature were -0.12, 0.08, -0.05, and 0.03, respectively. In the reducing sugar content prediction model, the regression coefficients for total sugar content before redrying, chloride ion content before redrying, and drying zone 3 temperature were 1.95, -0.90, and 0.60, respectively. For the total sugar content prediction model, the regression coefficients for total sugar content before redrying, chloride ion content before redrying, and drying zone 3 temperature were 2.20, -1.00, and 0.25, respectively. In conclusion, the prediction models for chloride ion, potassium oxide, and nicotine content after threshing and redrying demonstrated superior performance, whereas the models for total nitrogen, total sugar, and reducing sugar content were less effective. The temperature in the drying zones significantly influenced the routine chemical components of tobacco leaves after redrying.

Key words: flue-cured tobacco leaves, threshing and redrying, Lasso, prediction model, routine chemical components

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