重金属铜胁迫苹果砧木根系的显微拉曼光谱诊断研究

重金属污染会影响农作物的正常生长，如何快速准确的实现对农作物中重金属的检测已成为亟待解决的问题之一。传统植物中重金属检测依赖于化学方法，虽然可以实现重金属含量的精准检测，然而其操作过程繁琐，并且无法实现批量样本的检测，更无法实现重金属胁迫下植物组织的原位微观检测。拉曼光谱具备无损探测固体、液体和气体状态的分子振动信息、光谱分辨率高和对水分不敏感等优势，因此利用拉曼光谱技术检测农作物中重金属含量具有可行性。苹果砧木是苹果树幼苗嫁接的基础，能够保障后期的苹果树体健康以及苹果果品品质与产量，而苹果砧木根系受到重金属污染，阻碍其健康生长并影响苹果树幼苗的抗逆性，因此探明重金属与苹果砧木根系互作机理十分必要。该研究以5组不同浓度CuSO₄·5H₂O溶液胁迫下的苹果砧木为研究对象，首先采集不同铜离子（Cu2+）胁迫梯度下苹果砧木根系的拉曼散射光谱，利用自适应迭代重加权惩罚最小二乘法（Air-PLS）和S-G平滑方法对所获得的拉曼光谱数据进行预处理，去除荧光影响以及进行基线校正；其次建立偏最小二乘判别分析（PLS-DA）模型和支持向量机（SVM）判别模型，结果表明：基于显微拉曼光谱和SVM，PLS-DA判别模型对苹果砧木根系的铜离子胁迫进行判别，SVM模型准确率可达100%，PLS-DA模型准确率为96%，能够较好的预测出苹果砧木受重金属铜的胁迫程度；最后基于特征拉曼光谱峰1 096，1 329，1 605和2 937 cm-1进行苹果砧木根系横截面的化学成像可视化研究。研究结果表明，拉曼光谱技术结合Air-PLS和S-G平滑建立的SVM模型和PLS-DA模型可以快速、有效地进行苹果砧木根系受重金属胁迫程度的诊断，为重金属胁迫农作物检测提供新的思路，对农作物的重金属逆境胁迫互作机理诊断具有理论指导意义。

Microscopic Raman Spectroscopy for Diagnosing Roots in Apple Rootstock Under Heavy Metal Copper Stress

Heavy metal pollution will affect the normal growth of crops, and quickly detecting the content of heavy metals in crops has become a problem to be investigated. The traditional detection of heavy metals in plants depends on chemical methods. Although it can realize the high accurate detection of heavy metal content, its operation process is laborious, and it cannot meet the requirements of the high throughput detection, let alone the in-situ micro detection of plant tissues under heavy metal stress. Raman spectroscopy has the advantages of non-destructive detection of molecular vibration information of solid, liquid and gas species, high spectral resolution and insensitive to water. Therefore, it is feasible to monitor the content of heavy metals in crops by Raman spectroscopy. Apple rootstock is the basis of apple seedling grafting, which can ensure the health of the apple tree and apple quality and yield in the later stage. The root of apple rootstock is polluted by heavy metals directly, which hinders its healthy growth and affects the stress resistance of apple seedlings. Therefore, studying the interaction mechanism between heavy metals and apple rootstock root is necessary. In this study, five groups of apple rootstocks under the stress of CuSO₄·5H₂O solution with different concentrations were investigated. Firstly, the Raman scattering spectra of apple rootstocks under different copper ion (Cu2+) stress gradients were collected, and the adaptive iterative reweighting partial least squares (air-PLS) and S-G smoothing method were applied to preprocess the obtained raw Raman spectrum data for removing the fluorescence effect and correcting the baseline. Secondly, partial least squares discriminant analysis (PLS-DA) and support vector machine (SVM) discriminant models were established to estimate the different heavy metal stress concentrations. Results showed that the accuracy of the SVM model and PLS-DA model could reach 100% and 96%, respectively, which is promising for predicting apple rootstocks’ heavy metal Cu stress situation; finally, the chemical imaging was mapped based on the characteristic Raman spectrum peaks at 1 096, 1 329, 1 605 and 2 937 cm-1. It was illustrated that the Raman signal intensity increased first and then decreased with the increase of stress concentration in the exact wavenumber. These findings demonstrated the potential of micro-Raman scattering for measuring apple rootstock heavy stress, which provides anovel method for detecting heavy metal stress of crops.