基于高光谱技术的病害早期胁迫下黄瓜叶片中过氧化物酶活性的研究

发布时间：2018-03-12 21:25

本文选题：高光谱　切入点：黄瓜细菌性角斑病　出处：《光谱学与光谱分析》2017年06期 　论文类型：期刊论文

【摘要】：应用可见/近红外高光谱对细菌性角斑病早期胁迫下的黄瓜叶片中所含过氧化物酶(peroxidase,POD)活性进行检测。在380~1 030nm光谱范围获取120个样本(健康,病害轻微感染1级和2级)的光谱曲线,并使用分光光度计法测量感染病害样本中的过氧化物酶活性值。采用单因素方差分析(analysis of variance,ANOVA)对三种不同程度早期病害胁迫下过氧化物酶活性值进行统计分析,结果表明不同程度病害胁迫下黄瓜叶片中的过氧化物活性存在显著性差异(p=0.05)。采用SPXY方法将样本分为建模集(80个样本)与预测集(40个样本)。采用random frog(RF)和回归系数法(regression coefficient,RC)方法提取特征波段,并建立过氧化物酶活性值的偏最小二乘回归(partial least square regression,PLSR)预测模型。最终得到RF-PLSR具有最佳的预测效果,预测集相关系数为0.816,预测均方根误差为11.235。研究结果表明高光谱结合化学计量学方法可以实现细菌性角斑病早期胁迫下黄瓜叶片中过氧化物酶活性的测定,为植物病害的早期无损诊断提供参考。
[Abstract]:The activity of peroxidase peroxidase (POD) in cucumber leaves under early stress of bacterial keratoplakia was detected by visible / near infrared hyperspectral method. 120 samples (healthy) were obtained in the spectrum range of 380 ~ 1030nm. The spectral curves of the disease with slight infection of grades 1 and 2), The peroxidase activity in infected disease samples was measured by spectrophotometer, and the peroxidase activity was analyzed by single factor analysis of variance (ANOVA) under three different degrees of early disease stress. The results showed that there was significant difference in peroxide activity in cucumber leaves under different degree of disease stress. SPXY method was used to divide the samples into modeling set (80 samples) and prediction set (40 samples). Random frogfr and regression coefficient method were used. The characteristic bands were extracted by regression regression method. A partial least square regression model for predicting peroxidase activity was established. Finally, the best prediction effect of RF-PLSR was obtained. The correlation coefficient of prediction set was 0.816, and the root mean square error of prediction was 11.235. The results showed that hyperspectral combined with chemometrics could be used to determine the activity of peroxidase in cucumber leaves under early bacterial corner spot stress. It provides reference for early nondestructive diagnosis of plant diseases.
【作者单位】：浙江大学农业与生物技术学院生物技术研究所;浙江大学生物系统工程与食品科学学院;
【基金】：国家自然科学基金项目(31471417) 高等学校博士学科点专项科研基金项目(20130101110104)资助
【分类号】：O657.3;S436.421

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