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作物学报 ›› 2026, Vol. 52 ›› Issue (3): 922-935.doi: 10.3724/SP.J.1006.2026.54100

• 耕作栽培·生理生化 • 上一篇    下一篇

基于高光谱成像的烟叶主要化学成分无损检测与可视化

杨月1(), 张新新1,2, 贺增辉3, 李瑞东3, 潘昱洁3, 李嘉康1, 杜薇4, 徐大勇1, 堵劲松1,*()   

  1. 1中国烟草总公司郑州烟草研究院, 河南郑州 450001
    2郑州轻工业大学软件学院, 河南郑州 450002
    3云南烟叶复烤有限责任公司, 云南昆明 650021
    4四川中烟工业有限责任公司, 四川成都 610000
  • 收稿日期:2025-08-07 接受日期:2025-11-18 出版日期:2026-03-12 网络出版日期:2025-12-09
  • 通讯作者: *堵劲松, E-mail: djsdxx@126.com
  • 作者简介:E-mail: y.hean@foxmail.com
  • 基金资助:
    中国烟草总公司重大科技计划项目(110202401033(SZ-07));中国烟草总公司云南省公司科技计划重大项目(2024530000241026);云南烟叶复烤有限责任公司陆良复烤局科技计划项目(2023FK03);河南省科技攻关项目(252102211069)

Non-destructive prediction and visualization of major chemical components in tobacco leaves using hyperspectral imaging

Yang Yue1(), Zhang Xin-Xin1,2, He Zeng-Hui3, Li Rui-Dong3, Pan Yu-Jie3, Li Jia-Kang1, Du Wei4, Xu Da-Yong1, Du Jin-Song1,*()   

  1. 1Zhengzhou Tobacco Research of China National Tobacco Corporation, Zhengzhou 450001, Henan, China
    2School of Software, Zhengzhou University of Light Industry, Zhengzhou 450002, Henan, China
    3Yunnan Leaf Tobacco Redrying Co., Ltd., Kunming 650021, Yunnan, China
    4China Tobacco Sichuan Industrial Co., Ltd., Chengdu 610000, Sichuan, China
  • Received:2025-08-07 Accepted:2025-11-18 Published:2026-03-12 Published online:2025-12-09
  • Contact: *堵劲松, E-mail: djsdxx@126.com
  • Supported by:
    Major Science and Technology Program of China Tobacco Corporation(110202401033(SZ-07));Major Science and Technology Program of Yunnan Provincial Tobacco Company of China National Tobacco Corporation(2024530000241026);Science and Technology Program of Luliang Redrying Plant, Yunnan Tobacco Redrying Co., Ltd.(2023FK03);Science and Technology Research and Development Plan of Henan Province(252102211069)

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摘要：

烟叶的化学成分是决定其香气、风味与吸食质量的关键因素, 利用高光谱成像(hyperspectral Imaging, HSI)技术能够实现烟叶主要化学成分的快速、无损检测与可视化。本研究选取云南不同产区、不同等级的240份烟叶样本为研究对象, 采集967.05~2561.33 nm范围内的高光谱图像, 提出一种结合光谱差值与超像素聚类的感兴趣区域(region of Interest, ROI)提取方法, 有效剔除背景、叶脉及不规则结构干扰。光谱数据经标准正态变量变换(standard normal variate, SNV)与一阶导数(first derivative, FD)联合处理后, 采用主成分分析(principal component analysis, PCA)进行特征降维, 并基于偏最小二乘回归(partial least squares regression, PLSR)对烟碱、总糖、还原糖、总氮、钾和氯6种成分进行统一建模。结果表明, SNV+FD预处理能有效提升模型性能, 其中烟碱、总糖、还原糖和总氮的交叉验证决定系数(coefficient of determination for cross-validation, Q2)均超过0.89, 交叉验证均方根误差(root mean square error of cross-validation, RMSECV)最低达0.09; 在20个独立样本测试中, 6种成分的预测决定系数(coefficient of determination for prediction, R2)分别为0.930、0.908、0.854和0.915, 平均相对偏差(relative difference, RD)小于5%, 相对预测偏差(residual prediction deviation, RPD)均高于2.5, 验证了模型的稳定性与预测能力。基于所建模型实现了烟叶主要化学成分的可视化分布, 揭示了各成分在叶片不同区位的异质性特征。本研究提出的ROI提取与建模方法能够有效实现烟叶主要化学成分的无损检测与可视化, 可为烟叶品质评价与精细化加工提供技术支撑。

关键词: 高光谱成像, 无损检测, 化学成分, 感兴趣区域, 偏最小二乘回归, 烟草

Abstract:

The chemical composition of tobacco leaves plays a crucial role in determining their aroma, flavor, and smoking quality. Hyperspectral imaging (HSI) offers a rapid, non-destructive means of detecting and visualizing key chemical constituents in tobacco leaves. In this study, 240 flue-cured tobacco samples from various grades and production areas in Yunnan province, China, were analyzed. Hyperspectral images were collected across the 967.05-2561.33 nm spectral range, and an improved region of interest (ROI) extraction method—combining spectral difference analysis with superpixel clustering—was proposed to effectively eliminate interference from background, leaf veins, and irregular structures. Spectral data were preprocessed using a combination of standard normal variate (SNV) transformation and first derivative (FD), followed by principal component analysis (PCA) for dimensionality reduction. Partial least squares regression (PLSR) models were developed under a unified modeling framework to simultaneously predict six chemical components: nicotine, total sugars, reducing sugars, total nitrogen, potassium, and chlorine. The results showed that the SNV+FD preprocessing strategy could effectively enhance model performance. The coefficients of determination for cross-validation (Q2) for nicotine, total sugars, reducing sugars, and total nitrogen all exceeded 0.89, with the lowest root mean square error of cross-validation (RMSECV) reaching 0.09. In an external test with 20 independent samples, the coefficients of determination for prediction (R2) were 0.930, 0.908, 0.854, and 0.915, respectively. The average relative deviation (RD) was less than 5%, and the residual prediction deviation (RPD) values were all above 2.5, which verified the stability and predictive capability of the models. The established models enabled pixel-level visualization of chemical constituent distribution, revealing distinct heterogeneity across different leaf regions. The proposed ROI extraction and modeling method provides an efficient, non-destructive, and visual approach for evaluating tobacco quality and supporting precision processing.

Key words: hyperspectral imaging (HSI), non-destructive testing, chemical component, region of interest (ROI), partial least squares regression (PLSR), tobacco

图1

高光谱成像系统 A: 高光谱成像系统示意图; B: 高光谱成像系统实物图。"

图2

典型烟叶样本的感兴趣区域(ROI)提取过程 A: 不同区域光谱曲线对比; B: 高光谱假彩色图; C: 差值识别叶脉图; D: 感兴趣区域掩膜。"

表1

烟叶化学成分参考值统计结果"

化学成分
Chemical component		 平均值
Mean		 标准差
Standard deviation		 变异系数
Coefficient of variation		 最大值
Max.		 最小值
Min.
烟碱 Nicotine 2.45 0.65 26.53 4.59 1.33
总糖 Total sugars 35.33 4.64 13.13 43.37 18.22
还原糖 Reducing sugars 31.72 4.27 13.46 39.76 15.97
总氮 Total nitrogen 2.01 0.33 16.42 3.23 1.44
钾 Potassium 2.00 0.30 15.00 3.05 1.29
氯 Chlorine 0.27 0.21 77.78 1.28 0

图3

典型烟叶样本的不同ROI提取方法效果对比 A: 固定阈值提取方法; B: 主成分分割提取方法; C: K-means聚类提取方法; D: 本试验提出的方法; E: 本方法ROI提取前后光谱反射率平均值及误差带。"

图4

不同预处理光谱与前10个主成分对应波长 A: 原始光谱; B: 标准正态变换(SNV)预处理; C: 一阶导数(FD)预处理; D: 标准正态变换+一阶导数预处理。"

表2

不同预处理方法主成分分析结果"

预处理方法
Preprocessing method		 主成分
Principal component		 波长
Wavelength (nm)		 载荷值
Loading value		 贡献率
Contribution rate (%)
RAW PC1 1422.56 0.0814 81.77
PC2 1083.70 0.0975 17.19
PC3 1900.29 0.1598 0.62
PC4 2089.16 0.1434 0.26
PC5 1444.78 -0.1349 0.11
SNV PC1 1928.06 0.1404 65.22
PC2 1411.45 0.1499 25.70
PC3 1450.33 0.1602 7.54
PC4 1017.04 0.1113 0.60
PC5 1644.76 0.1270 0.35
FD PC1 1872.51 0.2968 3.57
PC2 1411.45 0.2237 15.76
PC3 2122.49 0.1721 7.22
PC4 1889.18 0.3345 1.63
PC5 1916.95 0.2346 0.66
SNV+FD PC1 1889.18 0.3541 54.40
PC2 1866.96 0.2746 34.25
PC3 1400.34 0.2338 8.74
PC4 1672.53 0.2128 0.88
PC5 1678.09 0.3012 0.51

表3

各化学成分不同预处理方法模型性能对比"

化学成分
Chemical
component		 预处理方法
Preprocessing method		 主成分数
Number of principal
components		 $R_{\text{c}}^{2}$ RMSEC $R_{\text{v}}^{2}$ RMSEP Q2 RMSECV
烟碱
Nicotine		 RAW 4 0.65 0.38 0.41 0.32 0.75 0.32
SNV 10 0.82 0.28 0.66 0.23 0.87 0.23
FD 15 0.92 0.18 0.88 0.17 0.94 0.17
SNV+FD 15 0.92 0.18 0.89 0.16 0.94 0.16
总糖
Total sugars		 RAW 5 0.83 1.90 0.80 1.97 0.83 1.93
SNV 10 0.85 1.81 0.83 1.82 0.87 1.66
FD 15 0.90 1.46 0.91 1.34 0.91 1.43
SNV+FD 17 0.91 1.42 0.90 1.38 0.90 1.48
还原糖
Reducing sugars		 RAW 5 0.83 1.74 0.73 2.10 0.83 1.75
SNV 9 0.85 1.64 0.80 1.83 0.89 1.44
FD 19 0.91 1.28 0.86 1.55 0.90 1.33
SNV+FD 12 0.91 1.31 0.85 1.56 0.90 1.36
总氮
Total nitrogen		 RAW 5 0.77 0.16 0.68 0.18 0.78 0.15
SNV 10 0.88 0.12 0.83 0.13 0.91 0.10
FD 12 0.92 0.09 0.88 0.11 0.93 0.09
SNV+FD 12 0.92 0.10 0.86 0.12 0.93 0.09
钾
Potassium		 RAW 5 0.37 0.24 0.09 0.23 0.35 0.24
SNV 10 0.49 0.22 0.50 0.17 0.51 0.21
FD 15 0.52 0.21 0.32 0.20 0.64 0.18
SNV+FD 14 0.55 0.20 0.51 0.17 0.62 0.18
氯
Chlorine		 RAW 5 0.15 0.20 0.33 0.12 0.23 0.19
SNV 10 0.36 0.18 0.24 0.13 0.64 0.13
FD 13 0.68 0.12 0.81 0.06 0.70 0.12
SNV+FD 14 0.69 0.12 0.57 0.10 0.71 0.12

表4

各化学成分不同预处理方法统一模型性能对比"

预处理方法
Preprocessing method		 化学成分
Chemical component		 主成分数
Number of principal components		 $R_{\text{c}}^{2}$ RMSEC $R_{\text{v}}^{2}$ RMSEP Q2 RMSECV
RAW 烟碱Nicotine 5 0.75 0.32 0.51 0.40 0.74 0.33
总糖Total sugars 5 0.83 1.90 0.80 1.97 0.83 1.93
还原糖Reducing sugars 5 0.83 1.74 0.73 2.10 0.83 1.75
总氮Total nitrogen 5 0.77 0.16 0.68 0.18 0.77 0.16
钾Potassium 4 0.37 0.24 0.09 0.23 0.35 0.24
氯Chlorine 3 0.14 0.20 0.22 0.13 0.16 0.20
SNV 烟碱Nicotine 10 0.82 0.28 0.66 0.34 0.80 0.29
总糖Total sugars 8 0.85 1.82 0.83 1.82 0.84 1.88
还原糖Reducing sugars 8 0.85 1.64 0.79 1.87 0.84 1.69
总氮Total nitrogen 10 0.88 0.12 0.83 0.13 0.87 0.12
钾Potassium 10 0.49 0.22 0.50 0.17 0.43 0.23
氯Chlorine 8 0.36 0.18 0.27 0.13 0.32 0.18
FD 烟碱Nicotine 16 0.93 0.17 0.90 0.18 0.82 0.18
总糖Total sugars 17 0.91 1.43 0.90 1.39 0.89 1.53
还原糖Reducing sugars 15 0.91 1.29 0.86 1.50 0.90 1.36
总氮Total nitrogen 18 0.94 0.08 0.91 0.09 0.92 0.09
钾Potassium 19 0.59 0.19 0.58 0.16 0.51 0.21
氯Chlorine 19 0.77 0.10 0.71 0.08 0.70 0.12
SNV+FD 烟碱Nicotine 19 0.93 0.17 0.93 0.15 0.92 0.18
总糖Total sugars 14 0.90 1.43 0.90 1.36 0.89 1.51
还原糖Reducing sugars 11 0.91 1.31 0.85 1.58 0.90 1.35
总氮Total nitrogen 19 0.94 0.08 0.91 0.09 0.93 0.09
钾Potassium 19 0.58 0.20 0.50 0.17 0.52 0.21
氯Chlorine 19 0.76 0.11 0.70 0.08 0.71 0.12

表5

外部样本模型性能指标"

化学成分
Chemical component		 R2 RD均值
RD mean (%)		 RSD
(%)		 RPD
(%)
烟碱Nicotine 0.930 4.91 1.35 3.90
总糖Total sugars 0.908 3.11 2.50 3.31
还原糖Reducing sugars 0.854 4.36 2.71 2.63
总氮Total nitrogen 0.915 3.63 2.59 3.43
钾Potassium 0.488 7.23 2.72 1.45
氯Chlorine 0.698 31.91 36.57 1.88

图5

烟叶主要化学成分预测模型性能 A: 训练集数据分布及拟合; B: 测试集数据分布及拟合; C: 模型残差分布。NIC: 烟碱; TS: 总糖; RS: 还原糖; Tkn: 总氮; K: 钾; Cl: 氯。"

图6

烟叶表面主要化学分布反演 A: 烟碱分布; B: 总糖分布; C: 还原糖分布; D: 总氮分布; E: 钾分布; F: 氯分布。"

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