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作 者:陈添兵[1] 姚明印[1] 周华茂[1] 林永增[1] 黎文兵[1] 郑美兰[1] 刘木华[1]
机构地区:[1]江西农业大学生物光电及应用重点实验室,江西南昌330045
出 处:《激光与红外》2014年第5期482-486,共5页Laser & Infrared
基 金:国家自然科学基金(No.31271612);江西省教育厅科技计划(No.CJJ12249);江西省学术带头人计划(No.09004004)项目资助
摘 要:利用波长1064 nm的Nd∶YAG脉冲激光器获取国家标准土壤中铅元素的诱导击穿等离子体,并用八通道光纤光谱仪采集了样品等离子体光谱。应用多种光谱预处理方法对光谱进行信息提取和分析,比较了光谱的预处理方法对偏最小二乘(PLS)模型定量预测能力的影响。结果表明,采用9点光谱平滑处理和多元散射校正(MSC)预处理的模型质量较好,校正集和预测集的相关系数R分别为0.9981和0.9948,交互验证均方根误差(RMSECV)和预测均方根误差(RMSEP)分别为12和17;元素Pb浓度分析测量的结果与标准值的相对偏差在10.5%以内,实验表明LIBS结合PLS建模能满足土壤中微量重金属快速检测的要求。Laser-induced breakdown plasma of Pb concentration in the national standard soil samples is obtained by u-sing 1 064 nm Nd ∶YAG laser,and plasma spectra of the samples are collected with fiber optic spectrome-ter.Quantitative calibration models for determination of Pb concentration in soil sample are established by partial least square (PLS)regression.The information is extracted and analyzed by applying a variety of spectral pretreatment methods.The effects of spectral pretreatment methods on the predicted ability of PLS models are discussed.The results show that models based on 9 spectral smoothing and multiplicative scatter correction have better quality.Correlation coefficients (R)of prediction for calibration samples and prediction samples are 0.9981 and 0.9948 respectively,root mean square error of cross validation (RMSECV)and root mean square error of prediction are 1 2 and 1 7 respective-ly.The deviation of Pb concentration between predicted value and actual measurement is within 1 0.5%.Therefore the feasibility of this PLS model for instant detection of heavy metals in soil is verified.
分 类 号:TN249[电子电信—物理电子学]
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