基于SiPLS模型的稻壳中重金属铬LIBS检测

发布时间：2018-06-30 02:59

  本文选题：光谱学 + 激光诱导击穿光谱　； 参考：《激光与光电子学进展》2016年11期

【摘要】：为了探索利用激光诱导击穿光谱(LIBS)对水田污染区稻壳中铬(Cr)元素含量进行绿色、快速检测的可行性,采用LIBS结合联合区间偏最小二乘法(SiPLS),对产自江西省某湖周边24个水田污染区稻壳样品中的Cr元素进行了定量分析。利用原子吸收光谱法(AAS)测得样品中Cr元素的真实浓度为32.51~510.33μg/g,利用LIBS光谱获得的Cr元素三个特征谱线Cr I 425.43nm、Cr I 427.48nm和Cr I 428.97nm清晰明显。对稻壳样品在422~446nm波段的LIBS光谱数据进行九点平滑处理后,在采用SiPLS获得的最佳模型基础上,得出模型交叉验证均方根误差与预测均方根误差分别为26.1μg/g和22.6μg/g,训练集相关系数与预测集相关系数分别为0.9714和0.9840。对预测集样品进行相对误差及T检验分析,结果显示稻壳中Cr元素浓度的预测值与AAS法测量的真实值之间的平均相对误差为6.17%,且无显著性差异,表明模型具有较好的预测精度,可为自然条件下生长的农产品重金属安全绿色分析提供参考依据。
[Abstract]:In order to explore the feasibility of using laser induced breakdown spectroscopy (LIBS) to detect the content of chromium (Cr) in rice husks in polluted paddy fields. A quantitative analysis of Cr in rice husk samples from 24 paddy fields around a lake in Jiangxi Province was carried out by using Libs combined with the combined interval partial least square (SiPLS) method. The true concentration of Cr in the sample was determined by atomic absorption spectrometry (AAS) to be 32.51 渭 g / g. The three characteristic lines of Cr I _ (425.43) nm ~ (-1) Cr I 427.48nm and Cr I 428.97nm obtained by Libs spectra were clear and obvious. After nine points smoothing the Libs spectral data of rice husk samples in 422~446nm band, the best model was obtained by using SiPLs. The results show that the root-mean-square error and the predicted RMS error of cross-validation are 26.1 渭 g / g and 22.6 渭 g / g, respectively, and the correlation coefficients of training set and prediction set are 0.9714 and 0.9840 respectively. The relative error and T test analysis of the predicted sample show that the average relative error between the predicted value of Cr concentration in rice husk and the true value measured by AAS method is 6.17, and there is no significant difference, which indicates that the model has good prediction accuracy. It can provide a reference for the safety and green analysis of heavy metals in agricultural products grown under natural conditions.
【作者单位】： 江西农业大学工学院;江西省高校生物光电及应用重点实验室;江西农业大学生物科学与工程学院;
【基金】：国家自然科学基金(31560482,31460419) 江西省自然科学基金重大科技项目(20143ACB21013) 2014年江西省远航工程计划(20140142) 江西省水稻产业技术体系专家项目(JXARS-02)
【分类号】：X835;S38;TN249
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本文编号：2084562