激光诱导击穿光谱结合神经网络测定土壤中的Cr和Ba  

Determination of Cr and Ba in Soil Using Laser Induced Breakdown Spectroscopy with Artificial Neural Networks

作　　者：沈沁梅[1] 周卫东[1] 李科学[1] 

机构地区：[1]浙江师范大学信息光学研究所,浙江金华321004

出　　处：《光子学报》2010年第12期2134-2138,共5页Acta Photonica Sinica

基　　金：浙江省自然科学基金(Y1100286);教育部科技计划重点项目(208056);浙江省高校重大科技攻关项目(ZD2009006);国家人事部留学人员科技活动项目择优资助

摘　　要：提出了一种基于人工神经网络的激光诱导击穿光谱技术实现元素成分高准确度定量分析的方法.采用基于动量和自适应学习速率梯度下降算法的反向传播神经网络,结合激光诱导击穿光谱技术的方法测定土壤中Cr和Ba元素的含量,得到了Cr和Ba的含量以及多次重复预测的相对标准偏差,并与采用传统的内标法得到的检测结果相比较.研究结果表明:基于动量和自适应学习速率梯度下降算法的反向传播神经网络分析方法,与激光诱导击穿光谱技术相结合能更好地实现对土壤样品中Cr和Ba元素的定量检测.相对内标法,神经网络分析方法与激光诱导击穿光谱技术相结合可以很明显地提高检测准确度和精密度,对采用激光诱导击穿光谱技术定量检测土壤重金属污染具有很好的应用价值.A laser-induced breakdown spectroscopy（LIBS） technique based on artificial neural networks（ANN） is proposed for high accuracy elemental quantitative analysis.A combination method of laser induced breakdown spectroscopy with an artificial neural networks is employed to predict the concentrations of Cr and Ba in soil samples.A back-propagation algorithm with momentum coefficient and adaptive learning rate is used and served as a calibration strategy for LIBS.The quantitative results and relative standard deviation of repeated predictions are obtained.The results are compared with those obtained by conventional calibration curve methods.The results presented demonstrate that the combination method of LIBS with ANN performs better than conventional calibration curve methods in quantitative detection of Cr and Ba in soil with improved accuracy and measurement precision in terms of relative standard deviation.Furthermore,it is an excellent method for LIBS quantitative detection for heavy metal in soils.

关 键 词：光谱学 定量检测 激光诱导击穿光谱 神经网络 土壤 重金属 

分 类 号：O433.4[机械工程—光学工程]