摘要
土壤的重金属污染影响农业和食品安全,对重金属污染的快速检测是一个亟需解决的问题。该研究应用激光诱导击穿光谱(Laser induced breakdown spectroscopy,LIBS)结合化学计量学方法对土壤中的镉(Cd)元素进行定量分析。根据土壤重金属污染的不同程度,制作含有Cd元素的15个浓度梯度的土壤样本,并采集各个样本的LIBS谱线。采用光谱预处理方法来减少试验误差和噪声。选取了Cd元素的分析谱线与分析谱线区间,建立基于偏最小二乘回归(Partial least squares regression,PLSR)和反向传播人工神经网络(Back propagation-artificial neural network,BPANN)方法 Cd元素含量回归模型。分析表明,非线性BP-ANN模型的预测性能优于线性PLSR模型,这可能是因为非线性模型能够通过自适应较好地解决土壤基体效应的影响。研究表明,LIBS技术结合多元化学计量学方法能够对土壤重金属镉进行准确检测,这为分析检测类似重金属元素提供思路,也为开发大田土壤营养元素和重金属检测仪器提供理论基础和技术支撑。
Heavy metal pollution in soil affects agriculture and food safety,and rapid detection of heavy metal pollution is an urgent problem to be solved. Laser induced breakdown spectroscopy( LIBS) couple with chemometrics methods were employed to conduct quantitative analysis of Cd content in soil. First,soil samples with 15 concentration gradients of Cd were manually made up. Then,the LIBS emission lines of all soil samples were collected. Preprocessing methods were used to eliminate errors and noise of spectral data. Then,characteristic lines and spectral regions of Cd were determined based on LIBS spectra. Quantity regression models based on partial least squares regression( PLSR) and back propagation-artificial neural network( BP-ANN)were set up and results were compared. As a result,models based on non-linear methods( BP-ANN) offered a promising results than linear methods of PLSR. Probable reason was that non-linear methods had an advantage to deal with matrix effects of soil automatically. Results indicated that LIBS coupled with multiple chemometrics methods could finished the detection of heavy metal Cd,which provided a brand-new analysis approach for heavy metals accurate detection in soil,and could offer theoretical foundation for development of heavy metal testing equipment.
作者
康盛
魏兆航
杨帆
赵艳茹
余克强
何东健
KANG Sheng;WEI Zhaohang;YANGFan;ZHAO Yanru;YU Keqiang;HE Dongjian(College of Mechanical and Electronic Engineeringt Northwest A&F University,Yangling Shannxi 712100,China;Key Laboratory of Agricultural Internet o f Things,Ministry of Agriculture and Rural A ffairs,Yangling Shannxi 712100,China;Shaanxi Key Laboratory of Agricultural Information Perception and Intelligent Service,Yangling Shannxi 712100,China)
出处
《农业工程》
2019年第10期38-42,共5页
AGRICULTURAL ENGINEERING
基金
国家自然科学基金项目(项目编号:61705188)
陕西省自然科学基础研究计划项目(项目编号:2017JQ3008)
中国博士后科学基金(项目编号:2017M613218)
西北农林科技大学中央高校基本科研业务费专项(项目编号:2452017125)
