油菜菌核病是一种真菌性病害, 可造成油菜产量严重减少, 而快速准确地进行病原物的早期侵染对于植物病害防治意义重大。 采用共聚焦拉曼光谱（500～2 000 cm-1波数范围内)技术结合化学计量学方法对油菜菌核病早期侵染进行判别分析。 采用共聚焦拉曼光谱仪采集健康和接种12 h核盘菌的油菜叶片表面拉曼光谱, 应用小波变换（wavelet transform, WT)进行拉曼光谱预处理以去除荧光背景。 并利用基于全谱范围的偏最小二乘判别分析（partial least squares-discriminant analysis, PLS-DA)的回归系数（regression coefficient, RC)进行特征峰的识别, 选出983, 1 001, 1 205, 1 521, 1 527, 1 658, 1 670和1 758 cm-1共八个特征峰用于建立PLS-DA模型进行油菜菌核病的早期侵染判别, 其识别准确率为100%。 结果表明: 拉曼光谱技术结合化学计量学方法能够实现油菜叶片中菌核病早期侵染的检测, 这为后续探究核盘菌与油菜叶片互作过程以及为进一步的病害早期监测和预防提供理论参考。

Oilseed rape(Brassica napus L. ) is a principal source of edible oil for human consumption and it feeds livestock as a by product with high energy and protein. However, oilseed plants often suffer from the invasion of various diseases, which could affect the yield and quality of the rapeseeds. Rape sclerotinia rot caused by the fungus sclerotinia sclerotiorum (Lib. ) de Bary may severely affect the growth of oilseed rape. Therefore, searching non-invasive detection methods of detection plant disease at early stage is crucial for monitoring growing conditions of crops. Confocal Raman spectroscopy in the region of 500～2 000 cm-1 coupled with chemometrics methods were employed to discriminate the rape sclerotinia disease at early stage on the oilseed rape leaves. A total of 60 samples(30 healthy plant leaves and 30 infected leaves) were used to acquire the Raman spectra and wavelet transform was applied to remove the fluorescence background. Regression coefficients of the partial least squares-discriminant analysis(PLS-DA) were used to select the 8 characteristic peaks based on the whole Raman spectra. 983, 1 001, 1 205, 1 521, 1 527, 1 658, 1 670 and 1 758 cm-1 were employed to establish PLS-DA discriminate models and recognition accuracy was 100%. The results showed Raman spectra combined with chemometrics method is promising for detecting rape sclerotinia infection in the oilseed rape leaves at early stage. This study provided a theoretical reference for researching the interaction between the fungus and plants and early detecting of disease infection.