基于拉曼光谱和自荧光光谱的柑橘黄龙病快速检测方法

doi:10.12133/j.smartag.2019.1.3.201812-SA026

Smart Agriculture ›› 2019, Vol. 1 ›› Issue (3): 77-86.doi: 10.12133/j.smartag.2019.1.3.201812-SA026

基于拉曼光谱和自荧光光谱的柑橘黄龙病快速检测方法

代芬^1,², 邱泽源¹, 邱倩¹, 刘楚健¹, 黄国增¹, 黄雅琳¹, 邓小玲^1,^2,^*()

^1. 华南农业大学电子工程学院,广东广州 510642
^2. 广东省农情信息监测工程技术研究中心,广东广州 510642

收稿日期:2018-12-19 修回日期:2019-03-10 出版日期:2019-07-30
基金资助:
国家自然科学基金(61675003);广东省自然科学基金(2018A030310153);广州市科技计划资助项目(201605030013);广州市科技计划资助项目(201707010346)
作者简介:代芬（1978-）,女,博士,副教授,研究方向：光谱分析在农业中的应用,Email: sunflower@scau.edu.cn。
通信作者:

Rapid detection of citrus Huanglongbing using Raman spectroscopy and Auto-fluorescence spectroscopy

Dai Fen^1,², Qiu Zeyuan¹, Qiu Qian¹, Liu Chujian¹, Huang Guozeng¹, Huang Yalin¹, Deng Xiaoling^1,^2,^*()

^1. College of Electronic Engineering of South China Agricultural University, Guangzhou 510642, China
^2. Guangdong Engineering Research Center for Monitoring Agricultural Information, Guangzhou 510642, China

Received:2018-12-19 Revised:2019-03-10 Online:2019-07-30

摘要/Abstract

摘要：

为了快速检测黄龙病这一柑橘毁灭性病害,分析了柑橘黄龙病样本和健康样本的自荧光和拉曼光谱差异,建立了基于自荧光光谱、拉曼光谱和混合光谱的PLS-DA模型,进行了模型的结果比较,最后绘制了三种模型的分类器特征曲线ROC,通过曲线下面积AUC参数进一步评价了模型的性能。试验结果表明,柑橘黄龙病叶片样本和健康叶片样本的自荧光光谱和拉曼光谱存在差异信息。在785nm波长激光诱导下,柑橘叶片样本都产生了比较强的自荧光。黄龙病叶片的自荧光相对于健康样本的自荧光在小于1203cm ^-1范围更弱,而在大于1206cm ^-1范围更强,其下降的斜率（绝对值）相对健康样本更小。在典型的黄龙病样本和健康样本的拉曼光谱数据中,均可发现具有以下拉曼峰且具有一致性：920cm ^-1,1160cm ^-1,1289cm ^-1,1331cm ^-1和1529cm ^-1。黄龙病样本和健康样本相比在1257cm ^-1、1396cm ^-1、1446cm ^-1、1601 cm ^-1和1622cm ^-1具有更大的拉曼峰值强度和光谱带宽,在1006cm ^-1、1160cm ^-1、1191cm ^-1和1529cm ^-1位置谱峰强度较弱,提示黄龙病样本的类胡萝卜素含量较低。基于自荧光光谱、拉曼光谱和混合光谱三种光谱的PLS-DA模型鉴别的准确率分别为86.08%、98.17%和94.75%。进一步计算三种模型的ROC曲线下面积AUC参数分别为0.9313、0.9991和0.9875,拉曼光谱模型的AUC值最大,也表明拉曼光谱模型的鉴别效果最优。拉曼光谱分析技术可以成为探索柑橘黄龙病快速诊断鉴别的新途径。

关键词: 黄龙病检测, 拉曼光谱, 自荧光光谱, PLS-DA, 柑橘, 诊断

Abstract:

In order to detect citrus Huanglongbing (HLB, also named citrus greening) quickly, Auto-fluorescence and Raman spectra of HLB leaf samples and healthy ones were collected and analyzed. PLS-DA models based on Auto-fluorescence spectra, Raman spectra and mixed spectra were established and compared respectively. Finally, ROC curves of the three models were drawn, and the performance of the models were further evaluated by using the area under curve AUC parameters. The results demonstrated spectral differences between Huanglongbing samples and healthy ones could be seen. With 785 nm laser irradiation, citrus leaf samples produced strong Auto-fluorescence and Raman peaks. The Auto-fluorescence of HLB leaves was weaker than that of healthy samples in the range of 800-1203 cm ^-1, but stronger in the range of 1206-1800 cm ^-1, and the slope of decline (absolute value) was smaller than that of healthy samples. The similar shapes were found in the Raman spectra of typical HLB samples and healthy ones. But the HLB samples had larger Raman peak intensity and spectral bandwidth at 1257 cm ^-1, 1396 cm ^-1, 1446 cm ^-1, 1601 cm ^-1 and 1622 cm ^-1 than healthy ones. The Raman peak intensity of HLB samples was weaker than that of healthy samples at 1006 cm ^-1, 1160 cm ^-1, 1191 cm ^-1 and 1529 cm ^-1 positions, suggesting that the carotenoid content of HLB samples was lower than healthy ones. The Auto-fluorescence model, the Raman spectral model and the mixed spectral model could distinguish two kinds of samples with the accuracy of 86.08%, 98.17% and 94.75%, respectively. Furthermore, AUCs of Receiver Operating Characteristic Curve (ROC) were calculated. The AUCs for the Auto-fluorescence model, the Raman spectral model and the mixed spectral model were0.9313、0.9991 and 0.9875, respectively. Through further analysis of ROC curve, the identification effect of the Raman spectral model was optimal. Raman spectroscopy could be a new way to explore the rapid diagnosis of citrus HLB.

Key words: Huanglongbing detection, Raman spectra, Auto-fluorescence spectra, PLS-DA, citrus, diagnose

中图分类号:

代芬, 邱泽源, 邱倩, 刘楚健, 黄国增, 黄雅琳, 邓小玲. 基于拉曼光谱和自荧光光谱的柑橘黄龙病快速检测方法[J]. 智慧农业, 2019, 1(3): 77-86.

Dai Fen, Qiu Zeyuan, Qiu Qian, Liu Chujian, Huang Guozeng, Huang Yalin, Deng Xiaoling. Rapid detection of citrus Huanglongbing using Raman spectroscopy and Auto-fluorescence spectroscopy[J]. Smart Agriculture, 2019, 1(3): 77-86.

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基于拉曼光谱和自荧光光谱的柑橘黄龙病快速检测方法

Rapid detection of citrus Huanglongbing using Raman spectroscopy and Auto-fluorescence spectroscopy

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