农作物锈病叶傅里叶变换红外光谱检测方法研究

doi:10.3964/j.issn.1000-0593(2019)02-0435-08

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摘要：在农作物生产中，不合理使用化学农药来防治植物病害的现象普遍存在，严重影响产品品质及食用安全，快速鉴别植物病害并采取合理的防治措施对提高农作物品质具有重要意义。利用红外光谱三级鉴别法（傅里叶变换红外光谱（FTIR）、二阶导数红外光谱（SD-IR）及二维相关红外光谱（2D-IR））对蚕豆、玉米、葱和蒜正常叶、锈病叶病斑处及病斑附近绿色部位进行了研究。结果显示，正常叶、病斑附近绿色部位及锈病叶病斑处的光谱吸收峰强度和形状存在微小差异。原始光谱中正常叶、病斑附近绿色部位及锈病叶病斑处的几个吸收强度比存在差异，蚕豆的正常叶、病斑附近绿色部位及锈病叶病斑处的吸收强度比A_{1 410}/A_{1 646}分别为0.698，0.624和0.616，A_{2 926}/A_{1 646}相应比值分别为0.665，0.638和0.552；玉米的相应比值A_{1 649}/A_{1 055}分别为0.813，0.696，0.691，A_{1 382}/A_{1 055}相应比值分别为0.552，0.478和0.465，A_{2 926}/A_{1 055}相应比值分别为0.574，0.467和0.469；葱的相应比值A_{1 382}/A_{1 061}分别为0.843，0.821和0.704，A_{2 923}/A_{1 061}相应比值分别为0.707，0.680和0.489；以上锈病叶病斑处及病斑附近绿色部位的几个峰强比均比正常叶小。二阶导数红外光谱在1 800～800 cm^-1范围内，正常叶、病斑附近绿色部位及锈病叶病斑处的吸收峰的形状及强度显示明显差异。二维相关红外光谱显示，正常叶、锈病叶病斑处及病斑附近绿色部位在860～1 690 cm^-1范围内自动峰和交叉峰的位置、数目及强度存在显著差异。蚕豆正常叶出现4个强自动峰，2组强的正交叉峰；病斑附近绿色部位出现5个强自动峰，4组强正交叉峰；锈病叶病斑处出现2个最强自动峰和5个中强自动峰，5组强正交叉峰；蚕豆锈病叶病斑处自动峰强度最强，而正常叶的各个自动峰的强度最低。玉米正常叶出现9个强自动峰，12组强的正交叉峰；病斑附近绿色部位出现11个强自动峰，3组最强的正交叉峰和11组中强正交叉峰；锈病叶病斑处出现6个强自动峰，3组强正交叉峰；蒜正常叶出现9个强自动峰，8组强的正交叉峰；病斑附近绿色部位出现2个最强自动峰和9个次强自动峰，10组强正交叉峰；锈病叶病斑处出现6个强自动峰，1组强正交叉峰；玉米和蒜病斑附近绿色部位的各个自动峰的强度最强，而锈病叶病斑处自动峰和交叉峰强度最弱。葱正常叶出现9个强自动峰，5组强的正交叉峰；病斑附近绿色部位出现8个强自动峰，3组强正交叉峰；锈病叶病斑处出现3个强自动峰，无正交叉峰出现。葱正常叶的各个自动峰的强度最强，而锈病叶病斑处自动峰强度最弱。结果表明，利用傅里叶变换红外光谱结合二阶导数红外光谱及二维相关红外光谱能简单、快速地鉴别研究农作物锈病叶，有望为农作物病害提供一种光谱检测方法。

关键词：农作物锈病；傅里叶变换红外光谱；二阶导数红外光谱；二维相关红外光谱

Abstract：In crop production, the unreasonable use of chemical pesticides to prevent from plant diseases is widespread, which affects product quality and food safety seriously. Therefore, it is of great significance to identify plant disease quickly and adopt appropriate control measures to improve the quality of crops. In this paper, the healthy leaves, rust spot and green area near the spot of rust diseased leaves of broad bean, corn, allium fistulosum and garlic were studied by a tri-step IR spectroscopy method, including Fourier transform infrared (FT-IR) spectroscopy, second derivatives infrared (SD-IR) spectroscopy and two-dimensional correlation infrared (2D-IR) spectroscopy. The results showed that tiny differences were observed in the intensities and shape of several peaks in the original spectra of each crop leaves. And several peak intensity ratios in the original spectra were different. The peak intensity ratio A_{1 410}/A_{1 646} of the healthy leaves, green area near spot and rust spot of rust diseased leaves of broad bean were 0.698, 0.624 and 0.616 respectively, and the corresponding ratio A_{2 926}/A_{1 646} were 0.665, 0.638 and 0.552 respectively. The corresponding ratio A_{1 649}/A_{1 055} of corn were 0.813, 0.696 and 0.691 respectively, and the corresponding ratio A_{1 382}/A_{1 055} were 0.552, 0.478 and 0.465 respectively; the corresponding ratio of A_{2 926}/A_{1 055} were 0.574, 0.467 and 0.469 respectively. The corresponding ratio A_{1 382}/A_{1 061} of allium fistulosum were 0.843, 0.821 and 0.704 respectively; the corresponding ratio of A_{2 923}/A_{1 061} were 0.707, 0.680 and 0.489 respectively. It can be seen that the intensity ratios of the rust spot and the green area near the spot of rust leaves were lower than that of the healthy leaves. More significant differences were exhibited in their SD-IR spectra in the range of 1 800～800 cm^-1, and clearer differences in the position and intensity of auto and cross peaks were observed in the range of 860～1 690 cm^-1 in 2D synchronous correlation spectra. The healthy leaves of broad beans showed 4 strong auto-peaks and 2 strong positive cross peaks, and 5 strong auto-peaks and 4 strong positive cross peaks were revealed in the green area near the spot of rust diseased leaves, and 2 strongest auto-peaks, 5 medium strong auto peaks and 5 strong positive cross peaks were appeared in the rust spot of rust diseased leaves. The intensity of auto-peaks of the rust spot of broad bean rust leaves were the strongest while the intensity of auto-peaks of the healthy leaves were the weakest. There were 9 strong auto-peaks and 12 strong positive cross peaks in the healthy leaves of corn, and 11 strong auto-peaks, 3 strongest positive cross peaks and 11 medium strong positive cross peaks in the green area near the rust spot on the diseased leaves, and 6 strong auto-peaks and 3 strong positive cross peaks in the rust spot of rust diseased leaves. There were 9 strong auto-peaks and 8 strong positive cross peaks in the healthy leaves of garlic, and 2 strongest auto-peaks, 9 medium strong auto-peak and 10 strong positive cross peaks appeared in the green area near spot of rust diseased leaves, and 6 strong auto-peaks and 1 strong positive cross peaks in the rust spot of rust diseased leaves. The intensity of auto-peaks of the green area near the spot of rust diseased leaves of corn and garlic were the strongest while the intensity of auto-peaks of the rust spot of rust diseased leaves were the weakest. There were 9 strong auto-peaks and 5 strong positive cross peaks in the healthy leaves of allium fistulosum, and 8 strongest auto-peaks and 3 strong positive cross peaks in the green area near the spot of rust diseased leaves, and 3 strong auto-peaks in the rust spot of rust diseased leaves. The auto-peaks of the healthy leaves of allium fistulosum were the strongest, while the auto-peaks of the rust spot of the rust leaves were the weakest. It is demonstrated that FT-IR combined with SD-IR and 2D-IR spectroscopy could be used to discriminate the crops rust leaves rapidly and effectively. Tri-step IR spectroscopy might provide a spectral method for detecting crop disease.

Key words：Crop rust; Fourier transform infrared (FT-IR) spectroscopy; Second derivative infrared (SD-IR) spectroscopy; Two-dimensional correlation infrared (2D-IR) spectroscopy

收稿日期: 2017-12-30 修订日期: 2018-04-21

中图分类号:

O657.3

基金资助: 国家自然科学基金项目（31760341）和云南省高校科技创新团队支持计划资助

通讯作者: 刘刚 E-mail: gliu66@163.com

作者简介: 杨卫梅，女，1992年生，云南师范大学物理与电子信息学院硕士研究生 e-mail: 1090887515@qq.com

引用本文:

杨卫梅，刘刚，刘雨，林浩坚，欧全宏，安冉，时有明. 农作物锈病叶傅里叶变换红外光谱检测方法研究[J]. 光谱学与光谱分析, 2019, 39(02): 435-442.
YANG Wei-mei, LIU Gang, LIU Yu, LIN Hao-jian, OU Quan-hong, AN Ran, SHI You-ming. Detection Method for Crop Rust by Fourier Transform Infrared Spectroscopy. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2019, 39(02): 435-442.

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https://www.gpxygpfx.com/CN/10.3964/j.issn.1000-0593(2019)02-0435-08 或 https://www.gpxygpfx.com/CN/Y2019/V39/I02/435