激光诱导击穿光谱检测青菜中镉元素的多变量筛选研究

利用激光诱导击穿光谱(LIBS)技术与常规化学分析方法获取28个浓度梯度含Cd元素的青菜样品的LIBS谱线信息以及Cd含量信息.对获取的光谱信息结合标准归一化处理(SNV)、一阶导数(FD)、二阶导数(SD)、中心化处理(Center)作为偏最小二乘法(PLS)模型的优选方法;再根据4种预处理方法的预测结果选取最佳方法,同时将该方法作为间隔偏最小二乘法(iPLS)与联合区间间隔偏最小二乘法(SiPLS)优选青菜LIBS谱线的最佳波长区间.结果表明:通过SiPLS优选的特征波长区间分别为214.72 ～ 215.82 nm,215.88～ 216.97 nm,225.08 ～ 226.35 nm,并且经过中心化预处理后建立的验证模型效果最好,结果显示交叉验证均方根误差(RMSECV)为1.487,验证均方根误差(RMSEP)为1.094,相关系数(R)为0.9942,平均相对误差(ARE)为11.60％.研究结果表明,所选优化方法适合青菜中重金属Cd元素的LIBS校正模型的建立,且具有较好的预测效果.

Detection of Cd in Chinese Cabbage by Laser Induced Breakdown Spectroscopy Coupled with Multivariable Selection

Heavy metal residue in vegetables is a big concern in the whole world.The aim of this work is to explore the effect of multivariable selection on analyzing Cd in Chinese cabbage polluted in lab by collecting the spectra of laser induced breakdown spectroscopy (LIBS) from the samples.At the same time,the actual Cd content in samples was obtained by anodic stripping voltammetry (ASV).The LIBS spectral range in partial least square (PLS) model was screened by standard normal variable transformation (SNV),first derivative (FD),second derivative (SD) and center treatment (CT) for preprocessing spectra and the optimized method was used for the analysis of interval partial least square (iPLS) and synergy interval partial least square (SiPLS).The results indicated that the method of CT was the best as a comparison with PLS,iPLS and SiPLS.And the intervals of wavelength were 214.72-215.82 nm,215.88-216.97 nm and 225.08 -226.35 nm by utilizing the optimized SiPLS.Here the root mean square error of cross validation (RMSECV) between real content and predicted ones was 1.487,the root mean squared error of prediction (RMSEP) was 1.094,the correlation coefficient (R) was 0.9942,and the average relative error (ARE) was 11.60％.The results displayed that LIBS could predict Cd in vegetables by multivariable selection of SiPLS and the accuracy could meet the requirement of rapid and green analysis of Cd in vegetables.