本文選題：高光譜　切入點：黃瓜細菌性角斑病　出處：《光譜學與光譜分析》2017年06期 　論文類型：期刊論文

【摘要】：應用可見/近紅外高光譜對細菌性角斑病早期脅迫下的黃瓜葉片中所含過氧化物酶(peroxidase,POD)活性進行檢測。在380~1 030nm光譜范圍獲取120個樣本(健康,病害輕微感染1級和2級)的光譜曲線,并使用分光光度計法測量感染病害樣本中的過氧化物酶活性值。采用單因素方差分析(analysis of variance,ANOVA)對三種不同程度早期病害脅迫下過氧化物酶活性值進行統(tǒng)計分析,結果表明不同程度病害脅迫下黃瓜葉片中的過氧化物活性存在顯著性差異(p=0.05)。采用SPXY方法將樣本分為建模集(80個樣本)與預測集(40個樣本)。采用random frog(RF)和回歸系數(shù)法(regression coefficient,RC)方法提取特征波段,并建立過氧化物酶活性值的偏最小二乘回歸(partial least square regression,PLSR)預測模型。最終得到RF-PLSR具有最佳的預測效果,預測集相關系數(shù)為0.816,預測均方根誤差為11.235。研究結果表明高光譜結合化學計量學方法可以實現(xiàn)細菌性角斑病早期脅迫下黃瓜葉片中過氧化物酶活性的測定,為植物病害的早期無損診斷提供參考。
[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.
【作者單位】： 浙江大學農業(yè)與生物技術學院生物技術研究所;浙江大學生物系統(tǒng)工程與食品科學學院;
【基金】：國家自然科學基金項目(31471417) 高等學校博士學科點專項科研基金項目(20130101110104)資助
【分類號】：O657.3;S436.421

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