基于SERS的苹果树腐烂病原菌早期侵染检测

Early infection detection of apple valsa canker pathogens based on SERS

为了早期诊断由黑腐皮壳真菌(Valsa mali Miyabe et Yamada)引起的苹果树腐烂病,该研究基于表面增强拉曼光谱(surface-enhanced Raman scattering,SERS)技术,以腐烂病菌丝、病原菌丝侵染的苹果树和健康的苹果树枝作为研究对象,结合S-G平滑和迭代自适应加权惩罚最小二乘法进行拉曼光谱预处理,经解析发现病原菌丝与染菌样本在1 598、1 595 cm^(-1)和2 930、2 925 cm^(-1)附近敏感谱峰明显区别于健康样本。重复试验分析发现,病原菌侵染可致寄主特征谱峰偏移以及谱峰强度改变:健康样本在1 286 cm^(-1)附近的特征峰随病原菌的侵染偏移至1 365 cm^(-1)附近;健康样本在1 286与1 587 cm^(-1)附近的谱峰强度比值小于0.5,染菌样本在1 365与1 595 cm^(-1)附近的谱峰强度比值大于0.5,而菌丝在1 327与1 598 cm^(-1)附近的谱峰强度比值大于1.0;1 595 cm^(-1)附近谱峰强度因染病而增强。构建BP神经网络模型进行早期染病样本的快速判别,识别率达90%以上。研究表明SERS技术结合BP神经网络可以准确识别苹果树腐烂病原菌丝,从而进行苹果树枝腐烂病的早期诊断,为植物病害的早期快速诊断和病害发生预警提供了研究思路和有效手段。

Apple valsa canker has caused the bark rot,and even seriously suffered the growth of fruit trees.Timely and accurately detection of pathogenic infestation is of great significance for the early disease warning,diagnosis,prevention and control measures.Surface-enhanced Raman spectroscopy(SERS)is very feasible in the detection of plant diseases,due mainly to its simple operation,high detection efficiency,and signal strength far beyond the conventional Raman technology.In this research,an accurate and rapid detection was performed on the early infection of apple valsa canker pathogens using SERS technology.The research objects were taken as the rotten bacterial filaments,the healthy and infected apple branches.S-G smoothing and adaptive iterative re-weighted penalized least squares(Air-PLS)were combined to carry out the de-noising and baseline correction of Raman spectra.The characteristic bands of apple branches and pathogenic were identified.Specifically,the healthy apple branches shared the outstanding characteristic peaks near 1286 and 1587 cm^(-1),whereas,the characteristic peaks of infected apple branches were near 1365,1595,and 2925 cm^(-1).The hypha of pathogenic bacteria had the outstanding characteristic peaks near 731,1327,1598,and 2930 cm^(-1).At the same time,15 groups of healthy,infected,and hyphal samples were selected,and found that the peak values of the characteristic peaks near 1286 and 1587 cm^(-1) of healthy apple branches were less than 0.5 on average.Meanwhile,the peak ratio of the characteristic peaks near 1365 and 1595 cm^(-1) of infected apple branches was greater than 0.5 in most cases,while the peak ratio of the characteristic peaks near 1327 and 1598 cm^(-1) of hypha was greater than 1 on average.In the vicinity of 1280-1380 cm^(-1),the difference between 1286 and 1365 cm^(-1) was 79,corresponding to the Raman peak generated by healthy and infected samples,while the difference between 1327 and 1365 cm^(-1) was 38,corresponding to the Raman peak generated by hypha samples and infected samples.The displacement ratio was close to 2:1.The peak intensity of infected apple branches near 1595 cm^(-1) was greater than that of healthy apple branches near 1587 cm^(-1) and that of hypha near 1598 cm^(-1).In general,there was the significant difference in the Raman spectra of healthy apple branches,infected apple branches,and hyphal samples.Back propagation artificial neural network(BP-ANN)was used to construct a qualitative discriminant model,while the BP binary classification model was constructed for the healthy and infected samples,where the recognition rate was up to 96.0%.The BP-four-classification model was also constructed for the healthy and samples with three degrees of infection,where the recognition rate reached 92.0%.In addition,the baseline prediction curve was contained the Raman spectral features in the process of baseline correction.BP-ANN binary and quadruple classification models were then constructed for the baseline prediction curve,where the accuracy was above 90%.Therefore,the SERS combined with BP-ANN can be expected to rapidly and accurately carry out the early diagnosis of apple valsa canker pathogen hypha infection.The finding can provide a technical idea for the early and rapid detection of plant diseases.