黄龙病危害柑橘果树日益严重, 对柑橘黄龙病进行快速检测研究具有重大意义。 采用拉曼光谱技术, 结合偏最小二乘判别分析(PLS-DA)方法探讨快速诊断柑橘黄龙病及病情类别的可行性。 获取柑橘叶片拉曼光谱并进行普通PCR鉴别分为轻度、 中度、 重度、 缺素和正常5类。 在715～1 639.5 cm-1范围内采用一阶导, 基线校正(Baseline)和多项式拟合三种方法扣除光谱背景, 突显叶片拉曼光谱特征峰。 多项式拟合方法分别进行了2次, 3次和4次拟合, 与一阶导和基线校正两种扣除背景方法进行比较, 结合最小二乘支持向量机(LS-SVM)和偏最小二乘判别分析(PLS-DA)建立判别模型。 经比较发现, 多项式拟合方法扣除光谱背景效果均好于另外两种方法, 其中用2次多项式拟合的PLS-DA模型的效果最好, 预测相关系数(RP)为0.98, 预测均方根误差(RMSEP)为0.67, 总误判率最小为0。 基线校正扣除光谱背景的LS-SVM模型效果最差, 总误判率最大为40%。 研究结果表明, 利用拉曼光谱技术对柑橘黄龙病进行快速识别研究具有一定的可行性, 为柑橘黄龙病无损检测研究提供一种新途径。

It is great significance to study the rapid detection for citrus greening because citrus greening is increasingly serious harmful for citrus fruit trees. In this paper, using Raman spectroscopy technology combined with partial least squares discriminant analysis (PLS-DA) method was used to explore the feasibility about rapid diagnosis citrus greening and the classification of disease. The Raman spectra of citrus leaves were obtained and leaves were divided into five types: slight greening, moderate greening, serious greening, nutrient deficiency and normal by common PCR. In the range of 715~1 639.5 cm-1, the three methods of first derivative, baseline correction and polynomial fitting were used to eliminate the spectral background to highlighted the characteristics peak of Raman spectra. Polynomial fitting were taken two times, three times and four times fitting in this method respectively, compared with the other two methods of first derivative and baseline correction for eliminated the spectral background. Combining with the least squares support vector machine (LS-SVM) and partial least squares discriminant analysis (PLS-DA), wedeveloped the discriminant models. By comparison, the effect of eliminated the spectral background using polynomial fitting was better than the other two methods. Especially the effect of PLS-DA model was taken two times fitting was the best The correlation coefficient of prediction (RP) was 0.98, while the root mean square error of prediction (RMSEP) was 0.67. The total misjudgment rate in the least was 0 andthe effect of LS-SVM model using the method of baseline correction was the worst, while the total misjudgment rate at maxium was 40%. The results showed that it was feasible to study the rapid identification of citrus greening by Raman spectroscopy technology, and a new approach to study the non-destructive detection of citrus greening was provided.