摘要
通过近红外光谱技术结合模式识别技术,建立重金属Hg、Cd和Pb污染水稻叶片的判别模型,以发展快速检测重金属污染水稻的技术。结果表明:在模拟稻田重金属Hg、Cd和Pb质量分数分别在1.5、1和500mg/kg条件下水稻正常生长发育;叶片近红外光谱数据通过小波函数(daubechies 2,db2)在0～5水平预处理后分别输入反向传递神经网络(back propagation neural networks,BPNN)和径向基神经网络(radial basis function neuralnetworks,RBFNN)预测的结果表明,小波转换采用db2函数第3分解水平对光谱的预处理结合径向基人工神经网络对重金属胁迫下水稻叶片识别效果最优,对Hg、Cd和Pb污染土壤上以及正常条件下生长的水稻叶片的识别正确率分别为95.5%,81.8%,91.3%和100.0%。这为近红外光谱分析技术在重金属污染水稻的识别上提供了初步依据,并有利于保障植物环境安全。
Summary There are hundreds of sources of heavy metal pollution, including the industries of coal, natural gas, paper, and mining. Toxic heavy metals, such as mercury, cadmium and lead, in air, soil, and water are global problems that are a growing threat to humanity. Rice is an important food crop in world, the rice polluted with heavy metal is seriously harmful to people＇ s health. There are many methods to detect the heavy metal, such as inductively coupled plasma-mass spectrometry （ICP MS）, inductively coupled plasma atomic emission spectrometer （ICP AES）, inductively coupled plasma optical emission spectrometry （ICP OES）, atomic absorption spectrometry （AAS）, X ray fluorescence spectrometry （XRF）, atomic fluorescence spectrometry （AFS） and so on. Although there are many advantages in the above technologies respectively, they are time consuming, high cost and sometimes require considerable analytic skill. Nowadays, as near infrared spectroscopy （NIR） responds to molecular energy transitions associated with hydrogen bonds of organic, while inorganic salts are not expected to directly influenceNIR spectra. To our interest, several studies have described useful NIR calibrations for minerals analysis. NIR spectra with supposed NIR-transparent minerals may be due to the association of cations with organic or hydrated inorganic molecules. Thus, in order to develop the fast detective technology on heavy metal polluted rice leaves, NIR was combined with pattern recognition to discriminate the mercury, cadmium and lead in polluted rice leaves. The rice was grown in paddy field polluted by mercury, cadmium and lead, the concentration of which was 1.5, 1 and 500 mg/kg respectively. After 50 days growth, the absorbance of near infrared spectroscopy of back of flag leaf was detected with Nicolet Nexus 870 （Thermo Corporation USA） and the data was collected with the software of Omnic 7.0. The acquired spectra of leaves with different heavy metal treatments were firstly pretreated with wavelet transform and then input in pattern recognition models of back propagation neural network （BPNN） and radical basis function neural network （RBFNN）. It was shown that the rice could grow, blossom and bear fruit in mercury, cadmium and lead polluted paddy field, the concentration of which was 1.5, 1 and 500 mg/kg respectively. The spectra of rice leaves were firstly pretreated with wavelet db2 function at 0-5 level, and then calculated with back propagation neural network （BPNN） and radical basis function neural network （RBFNN） model. It was shown that the pretreatment of db2 function at 3 level combined with RBFNN model was best. And the correct classification rates of rice in mercury, cadmium and lead polluted soil and control soil were 95.5%, 81.8%, 91.3% and 100.0% respectively. Our results indicated that it should be feasible to develop useful calibration models for the prediction of heavy metal in rice leaves. The performance of RBFNN model was best in the prediction of heavy metal （mercury, cadmium and lead） polluted rice leaves. It has also provided a basis of NIR on the recognition of heavy metal polluted rice, and then ensure the safety of plant environment.
出处
《浙江大学学报（农业与生命科学版）》
CAS
CSCD
北大核心
2013年第1期50-55,共6页
Journal of Zhejiang University:Agriculture and Life Sciences
基金
浙江省重点科技创新团队--农产品安全标准与检测技术科技创新团队资助项目(2010R50028)
"十二五"国家科技支撑计划资助项目(2012BAK17B03)
"十一五"国家科技支撑计划:食品安全关键技术--粮油
蔬果等安全控制技术的研究资助项目(2006BAK02A18)
关键词
水稻叶片
近红外光谱
重金属
小波转换
反向传递神经网络
径向基神经网络
rice leaves
near infrared spectroscopy
heavy metal
wavelet transform
back propagation neuralnetworks
radial basis function neural networks
