Analysis of chemical indicators of tobacco leaves in Jiangxi Province and construction of sensory quality classification model

doi:10.14088/j.cnki.issn0439-8114.2024.10.028

Abstract

Abstract: In order to explore the quality characteristics of tobacco leaves in Jiangxi Province, statistical methods such as analysis of variance and interval estimation were used to analyze the differences and coordination of 11 chemical indicators in the upper, middle, and lower parts of tobacco leaves with different sensory quality grades （Class A, Class B, Class C） in Jiangxi Province. On this basis, this study constructed support vector machine （SVM） models and random forest （RF） models for predicting the sensory quality classification of tobacco leaves. The results showed that Class C tobacco leaves in the upper, middle, and lower parts had 7, 6, and 6 chemical indicators higher than Class A and Class B, respectively. Class B tobacco leaves in the upper, middle, and lower parts had 3, 3, and 2 chemical indicators higher than Class A and Class C, respectively, while Class A tobacco leaves in the upper, middle, and lower parts had 0, 2, and 3 chemical indicators higher than Class B and Class C, respectively; from the perspective of interval length, class C tobacco leaves in the upper, middle, and lower parts had 9, 9, and 7 chemical indicators higher than Class A and Class B, respectively. Class B tobacco leaves in the upper, middle, and lower parts had 1, 0, and 3 chemical indicators higher than Class A and Class C, respectively, while Class A tobacco leaves in the upper, middle, and lower parts had 1, 2, and 1 chemical indicators higher than Class B and Class C, respectively. The coordination of chemical components in Class C tobacco leaves was much worse than that in Class A and Class B, which might be an important reason for the deterioration of sensory quality. The weighted average of accuracy, recall, and F1 score for both SVM and RF models exceeded 84%, and the SVM model had slightly higher three indicators than the RF model. There were significant differences in the chemical index characteristics of Class C tobacco leaves compared to Class A and Class B, while the differences between Class A and Class B were relatively small; the SVM model had better classification performance for Class A and B tobacco samples than the RF model, while the RF model had better recognition performance for Class C than the SVM model.

Key words: tobacco leaves, chemical indicators, sensory quality, classification model, Jiangxi Province

CLC Number:

S572

HUANG Jian, YANG Xin-shi, TANG Min, MA Zhan-feng, GUO Xian-feng, NING Yang, KONG Fan-yu, WANG Da-bin. Analysis of chemical indicators of tobacco leaves in Jiangxi Province and construction of sensory quality classification model[J]. HUBEI AGRICULTURAL SCIENCES, 2024, 63(10): 153-159.

References

[1] 欧阳文. 卷烟工艺与评吸[M]. 成都: 西南财经大学出版社, 2008.
[2] 尹启生, 过伟民, 张艳玲, 等. 烤烟品种间理化特征的差异及其与感官质量的关系[J]. 烟草科技, 2016, 49(5): 23-29.
[3] 曹仕明, 高远峰, 曹勤华, 等. 湖北典型生态区烤烟质量风格特征及其影响因子分析[J]. 中国烟草科学, 2015, 36(1): 14-18.
[4] 杨景全, 于国锋, 冯媛, 等. 初烤烟叶颜色与常规化学成分及感官质量相关性研究[J]. 江西农业学报, 2019, 31(5): 79-83.
[5] 赵友根, 拓阳阳, 李贵刚, 等. 复烤烟叶C3F等级主要化学成分与感官评吸质量的相关性[J]. 贵州农业科学, 2018, 46(9): 126-129.
[6] 王芳, 王玉平, 杨辉, 等. 贵州有机生态烟叶主要化学成分与感官质量的相关分析研究[J]. 中国农学通报, 2013, 29(22): 103-108.
[7] 王育军, 周冀衡, 李强, 等. 曲靖烟叶化学成分可用性及其对感官评吸质量的影响[J]. 烟草科技, 2014(11): 67-73.
[8] 武广鹏, 李许涛, 李钦奎, 等. 河南烟叶化学成分与感官质量的相关性分析[J]. 江西农业学报, 2022, 34(10): 25-29.
[9] 焦绍赫, 何宽信, 王念磊, 等. 江西烤烟主要理化指标对感官评吸质量的影响[J]. 贵州农业科学, 2018, 46(1): 21-24.
[10] 沈晗, 杨凯, 任伟, 等. 影响上部烟叶感官质量的主要化学成分分析[J]. 中国烟草学报, 2019, 25(6): 18-26.
[11] ZHANG J, TIAN F, YANG S, et al.An intelligent and automatic control method for tobacco flue-curing based on machine learning[J]. International journal of robotics and automation technology, 2016, 31: 509-518.
[12] 邱昌桂, 孔兰芬, 杨式华, 等. 基于GA-SVM算法的烤烟香型自动识别研究[J]. 烟草科技, 2019, 52(2): 101-108.
[13] 鲁梦瑶, 周强, 姜舒文, 等. 基于深度学习与多尺度特征融合的烤烟烟叶分级方法[J]. 中国农机化学报, 2022, 43(1): 158-166.
[14] LI R, ZHANG X, LI K, et al.Nondestructive and rapid grading of tobacco leaves by use of a hand-held near-infrared spectrometer, based on a particle swarm optimization-extreme learning machine algorithm[J]. Spectroscopy letters, 2020, 53(9): 685-691.
[15] 张慧, 张文伟, 张永毅, 等. 基于高光谱与纹理融合的烤烟分类方法研究[J]. 中国烟草学报, 2022, 28(3): 72-80.
[16] 李智慧, 梅吉帆, 李辉, 等. 高光谱成像的非烟物质分类识别研究[J]. 中国烟草学报, 2022, 28(3): 81-88.
[17] 李鑫, 汤卫荣, 张永辉, 等. 基于高光谱成像技术的烟叶田间成熟度判别模型[J]. 烟草科技, 2022, 55(7): 17-24.
[18] 邓建强,王大彬,乾艳,等.基于高光谱成像技术的烤烟上部烟叶成熟度光谱特征分析及判别模型构建应用研究[J].中国烟草学报,2024,30(1):36-45.
[19] LI J X, ZHAO H, ZHU S P, et al.An improved lightweight network architecture for identifying tobacco leaf maturity based on deep learning[J]. Journal of intelligent & fuzzy systems, 2021, 41(2): 4149-4158.
[20] ZHU H, CHU B, ZHANG C, et al.Hyperspectral imaging for presymptomatic detection of tobacco disease with successive projections algorithm and machine-learning classifiers[J]. Scientific reports, 2017, 7(1): 4125.
[21] 别瑞, 周婷云, 周显升, 等. 基于XGBoost算法的山东烟叶质量预测模型初探[J]. 中国烟草科学,2022,43(5): 80-86.
[22] BREIMAN L.Random forests[J]. Machine learning,2001,45(1):5-32.
[23] 许威, 宋纪真, 谌剑, 等. 赣州烟叶主要化学成分最优适宜范围研究[J]. 安徽农业科学, 2023, 51(2): 199-205, 211.
[24] 李晓, 万应发, 刘建永, 等. 江西黎川基地烟叶质量特点与稳定性分析[J]. 中国烟草科学, 2013, 34(3): 89-93.
[25] 王得强, 曾兵, 陈若星, 等. 江西烟叶质量特性分析[J]. 安徽农业科学, 2019, 47(17): 201-203, 206.
[26] 王聪,姜舒文, 黄坤, 等. 机器学习在农产品供应链关键环节中的应用进展研究综述[J]. 中国农机化学报,2021,42(5): 182-190.