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中国农学通报

中国农学通报 ›› 2024, Vol. 40 ›› Issue (27): 126-133.doi: 10.11924/j.issn.1000-6850.casb2024-0036

• 食品·营养·检测·安全 • 上一篇    下一篇

基于BP神经网络的雪茄原料感官质量预测模型构建

侯冰清1(), 王硕立2, 张友杰1, 曹阳1, 时向东2, 丁松爽2, 刘冰洋2, 王以慧1()   

  1. 1 山东中烟工业有限责任公司，济南250014
    2 河南农业大学烟草学院/国家烟草栽培生理生化研究基地/烟草行业烟草栽培重点实验室，郑州 450046
  • 收稿日期:2024-01-15 修回日期:2024-04-02 出版日期:2024-09-25 发布日期:2024-09-24
  • 通讯作者:
    王以慧，男，1971年出生，山东昌邑人，工程师，硕士，研究方向：雪茄烟配方工艺与雪茄烟原料研究。通信地址：250014 山东省济南市历下区经十路11888号，E-mail：。
  • 作者简介:

    侯冰清，男，1989年出生，河南洛阳人，助理工程师，硕士，研究方向：雪茄烟配方工艺与雪茄烟原料。通信地址：250014 山东省济南市历下区经十路11888号，E-mail：。

  • 基金资助:
    山东中烟工业有限责任公司科技项目“南阳雪茄原料生产技术研究”(2022370000340544)

Prediction Model of Sensory Quality of Cigar Raw Materials Based on BP Neural Network

HOU Bingqing1(), WANG Shuoli2, ZHANG Youjie1, CAO Yang1, SHI Xiangdong2, DING Songshuang2, LIU Bingyang2, WANG Yihui1()   

  1. 1 Shandong China Tobacco Industry Co., Ltd., Jinan 250014
    2 College of Tobacco Science, Henan Agricultural University/ National Tobacco Cultivation and Physiology and Biochemistry Research Center/ Key Laboratory for Tobacco Cultivation of Tobacco Industry, Zhengzhou 450046
  • Received:2024-01-15 Revised:2024-04-02 Published:2024-09-25 Online:2024-09-24

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摘要：

本研究旨在利用BP神经网络技术，深入分析并预测雪茄原料的常规化学成分与其感官质量之间的复杂关系。通过收集四川、湖北、云南、湖南和尼加拉瓜雪茄烟叶常规化学成分数据作为输入变量，结合雪茄原料各项感官质量指标作为输出变量，成功构建了拓扑结构为6-9-1的BP神经网络模型。该模型不仅能够准确预测雪茄原料的感官质量评吸结果，而且揭示了不同产区雪茄烟叶在化学成分和感官质量方面的独特特征。研究表明，所检测样本中，国内4个主产区雪茄烟叶总糖、还原糖、烟碱、氯含量均高于尼加拉瓜烟叶，尼加拉瓜烟叶香气质和香气量得分较高。四川烟叶刺激性得分较低，湖北产区雪茄烟叶余味得分较高，云南烟叶杂气得分较低，湖南烟叶燃烧性和灰色得分较高。本研究雪茄烟叶样本的常规化学成分和感官质量指标统计特征较好，基本服从正态分布。所构建的BP神经网络模型的预测值与实际值间差异较小，其中余味、刺激性、灰色和总分的相关系数均在0.9以上。在训练集、验证集和测试集的预测值和实际值误差中，除总分误差区间较大外，剩余多数指标误差区间在0~0.5范围内的比例达到85%以上。BP神经网络所建立的雪茄原料感官质量预测模型拟合效果较好。本研究的成功实施为基于常规化学成分快速、准确地预测雪茄原料感官质量提供了有力支持，有助于推动中式雪茄烟行业的创新发展。

关键词: 雪茄原料, 常规化学成分, 感官质量, BP神经网络模型, 预测模型

Abstract:

By establishing BP neural network model, the relationship between conventional chemical components and sensory quality of cigar raw materials was explored, in order to predict sensory quality of cigar raw materials quickly and accurately. With the content of conventional chemical components in cigar leaves from Sichuan, Hubei, Yunnan, Hunan and Nicaragua as input variables and the sensory quality indexes of cigar raw materials as output variables, BP neural network models with topological structure of 6-9-1 were constructed respectively to predict the sensory quality evaluation results of cigar raw materials. The results showed that in the samples tested, the contents of total sugar, reducing sugar, nicotine and chlorine in cigar leaves from four major producing areas in China were higher than those of Nicaraguan tobacco leaves. Nicaraguan tobacco leaves scored higher in aroma quality and volume of aroma, Sichuan tobacco leaves scored lower in irritation, Hubei cigar leaves scored higher in aftertaste, Yunnan tobacco leaves scored lower in impurity, and Hunan tobacco leaves scored higher in combustibility and gray. In this study, the statistical characteristics of conventional chemical components and sensory quality indexes of cigar tobacco samples were good, basically following normal distribution. The difference between the predicted value and the actual value of the BP neural network model was small, among which the correlation coefficients of aftertaste, irritation, gray and total score were all above 0.9. Among the errors of the predicted and actual values of the training set, verification set and test set, except the error interval of the total score was large, the error interval of the remaining most indexes was more than 85% within the range of 0-0.5. The prediction model of the sensory quality of cigar materials established by BP neural network has a good fitting effect, which can be used to predict the sensory quality of cigar materials based on conventional chemical components, and promote the innovative development of Chinese cigar.

Key words: cigar raw materials, conventional chemical composition, sensory quality, BP neural network model, prediction model