欢迎访问《中国农学通报》,

中国农学通报 ›› 2025, Vol. 41 ›› Issue (34): 121-128.doi: 10.11924/j.issn.1000-6850.casb2025-0091

• 食品·营养·检测·安全 • 上一篇    下一篇

基于感官质量稳定性的河南浓香型烟叶复烤干燥参数控制技术的研究

徐帅华1(), 党霞1, 楚晗1, 李秋剑2, 张攀峰1, 陈溪1, 丁易飞1, 王选1, 张相辉1()   

  1. 1 天昌国际烟草有限公司,河南许昌 461000
    2 浙江中烟工业有限责任公司,杭州 310000
  • 收稿日期:2025-02-12 修回日期:2025-09-26 出版日期:2025-12-04 发布日期:2025-12-04
  • 通讯作者:
    张相辉,男,1984年出生,河南许昌人,工程师,硕士,主要从事打叶复烤工艺技术研究。E-mail:
  • 作者简介:

    徐帅华,男,1990年出生,河南周口人,工程师,硕士,主要从事打叶复烤设备与工艺技术研究。E-mail:

  • 基金资助:
    河南省烟草公司重点科技项目“基于片型分类应用的高端原料价值挖掘技术开发”(豫烟办〔2024〕4号)

Research on Re-drying Parameter Control Technology for Strong Flavor Type Tobacco Leaves in Henan Based on Sensory Quality Stability

XU Shuaihua1(), DANG Xia1, CHU Han1, LI Qiujian2, ZHANG Panfeng1, CHEN Xi1, DING Yifei1, WANG Xuan1, ZHANG Xianghui1()   

  1. 1 Tianchang International Tobacco Co., Ltd., Xuchang, Henan 461000
    2 Zhejiang Tobacco Industry Co., Ltd., Hangzhou 310000
  • Received:2025-02-12 Revised:2025-09-26 Published:2025-12-04 Online:2025-12-04

摘要:

为探究复烤机干燥参数对河南浓香型烟叶烤后烟叶感官质量稳定性的影响,本研究以许昌‘中烟100’C3F烟叶为材料,研究不同干燥曲线对烤后烟叶感官质量的作用,旨在确定适于河南浓香型烟叶的干燥曲线。研究过程中,全面采集打叶复烤生产过程中干燥参数、来料参数、环境参数,并对复烤干燥后烟叶进行感官质量评价。最后,运用BP神经网络算法构建干燥参数控制模型,以此预测感官评吸结果。结果表明,采用低温慢烤工艺技术,干燥曲线采用抛物线型定温方式,将干燥一区、二区、三区温差控制在(4.0±1.0)℃,三区、四区、五区、六区温差控制在(2±1)℃,干燥区总温度控制在(380±5.0)℃。同时,将左右冷房含水率控制在(9.0±0.5)%,水分差值设定为±0.8%时,烤后烟叶香气质、香气量均得到显著提升,烟气细腻程度有所改善,余味也有不同程度的优化。利用BP神经网络模型算法对烟叶内在质量进行预测,烤后烟叶感官质量与预测模型感官评吸结果基本一致。表明该模型在参数改变时能够及时预测结果,为生产提供有效的指导。

关键词: 浓香型烟叶, 复烤, 干燥参数, 低温慢烤, 感官质量, 稳定性, BP神经网络

Abstract:

To study the influence of drying parameters of the re-drying machine on the sensory quality stability of Henan strong-aroma type flue-cured tobacco leaves after re-drying, ‘Zhongyan 100’ C3F was used as the experimental material. The effects of different drying curves on the sensory quality of re-dried tobacco leaves were investigated to determine the optimal drying curve suitable for Henan strong-aroma type flue-cured tobacco leaves. During the production process of threshing and re-drying, drying parameters, incoming material parameters, and environmental parameters were collected, and sensory quality evaluations were conducted on the re-dried tobacco leaves. Finally, a drying parameter control model was constructed using the BP neural network algorithm to predict the results of sensory evaluation. The results showed that by applying low-temperature slow roasting technology, adopting a parabolic constant temperature mode for the drying curve, controlling the temperature difference between drying zones 1, 2, and 3 within (4.0±1.0)℃, the temperature difference between drying zones 3, 4, 5, and 6 within (2±1)℃, the total temperature of the drying zone within (380±5.0)℃, and maintaining the moisture content in the left and right cold rooms at (9.0±0.5)% with a moisture difference of ±0.8%, both the aroma quality and aroma quantity of the re-dried tobacco leaves were improved, the fineness of the smoke was enhanced, and the aftertaste was improved to varying degrees. By using the model algorithm to predict the intrinsic quality of tobacco leaves, the sensory quality of the re-dried tobacco leaves within the module was basically consistent with the sensory evaluation results predicted by the model. When parameters are adjusted, the predicted results can be used to guide production in a timely manner.

Key words: strong-flavor tobacco, re-drying, drying parameter, low-temperature slow roasting, sensory quality, stability, BP neural network