激光诱导击穿光谱检测青菜中镉元素的多变量筛选研究

引用本文: 杨晖, 黄林, 刘木华, 陈添兵, 王彩虹, 姚明印. 激光诱导击穿光谱检测青菜中镉元素的多变量筛选研究[J]. 分析化学, 2017, 45(2): 238-244. doi: 10.11895/j.issn.0253-3820.160679 shu

Citation: YANG Hui, HUANG Lin, LIU Mu-Hua, CHEN Tian-Bing, WANG Cai-Hong, YAO Ming-Yin. Detection of Cd in Chinese Cabbage by Laser Induced Breakdown Spectroscopy Coupled with Multivariable Selection[J]. Chinese Journal of Analytical Chemistry, 2017, 45(2): 238-244. doi: 10.11895/j.issn.0253-3820.160679 shu

激光诱导击穿光谱检测青菜中镉元素的多变量筛选研究

1.
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, 南昌 330045;
2.
江西农业大学工学院生物科学与工程学院, 南昌 330045;
3.
江西农业大学工学院, 南昌 330045
基金项目:
本文系国家自然科学基金项目（Nos.31560482、31460419）、江西省自然科学基金重大科技项目（No.20143ACB21013）、江西省远航工程计划项目（No.20140142）、江西省水稻产业技术体系专家项目（No.JXARS-02）资助

摘要: 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。

关键词:

English

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

1.
College of Engineering, Jiangxi Agricultural University, Nanchang 330045, China;
2.
Key Laboratory of Optics-Electrics Application of Biomaterials of Jiangxi Province Higher Education, Nanchang 330045, China;
3.
College of Bioscience and Bioengineering, Jiangxi Agricultural University, Nanchang 330045, China
Received Date: 11 September 2016
Revised Date: 21 November 2016
Fund Project:
This work was supported by the National Natural Science Foundation of China (Nos.31560482, 31460419)

Abstract: 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.

Key words:

1. [1]
  JIA Chao-Hua, YAN Xin-Pei, GONG Xin, HUANG Zhi-Ren, JIANG Shi-Meng, JIANG Yong-Bing, LEI Ming, LONG Tang-Zhong. Chinese Agricultural Science Bulletin,2016,32(5):106-112 贾超华, 颜新培, 龚昕, 黄仁志, 蒋诗梦, 蒋勇兵, 雷鸣, 龙唐忠.中国农学通报,2016,32(5):106-112
2. [2]
  DONG Feng-Guang, WANG Zhao-Xia, GONG Chun-Bo, LIU Ze-Jing, SUN Zhao-Peng, SUN Yue-Lin. Journal of Food Safety and Quality,2016,7(7):3000-3005 董峰光, 王朝霞, 宫春波, 刘泽静, 孙兆鹏, 孙月琳.食品安全质量检测学报,2016,7(7):3000-3005
3. [3]
  ZENG Fang-Jun, YANG Zhi-Hai. Journal of Emcc,2016,26(1):80-83 曾凡俊, 阳枝海.中国环境管理干部学院学报,2016,26(1):80-83
4. [4]
  YUAN Lie-Jiang, YANG Meng-Xin, LI Meng-Li, LI Zhong-Hai. Food & Machinery,2015,31(1):59-63 袁列江, 杨梦昕, 李萌立, 李忠海.食品与机械,2015,31(1):59-63
5. [5]
  Shaheen N, Irfan N M, Khan I. Chemosphere,2016,152:431-438
6. [6]
  Anwar S, Muhammad F N, Sadaf G, Muhammad R, Shafaqat A, Arshaad K. Environ. Monit. Assess.,2016,188:541
7. [7]
  GONG Meng-Dan, ZHU Wei-Qin GU Yan-Qin, LI Shu-Yin, Jianati Tunike. Environ. Sci.,2016,37(6):2329-2337 龚梦丹, 朱维琴, 顾燕青, 李淑英, 加那提·吐尼克.环境科学,2016, 37(6):2329-2337
8. [8]
  LIU Yao-Lan, CHEN Huan-Sheng, JIANG Jian-Hua, HU Hua-Ai. Journal of Anhui Agri. Sci.,2011,39(20):12310-12312, 12314 刘尧兰, 陈焕晟, 蒋建华, 胡华爱.安徽农业科学,2011,39(20):12310-12312, 12314
9. [9]
  Ye X, Xiao W, Zhang Y, Zhao S, Wang G, Zhang Q, Wang Q. Environ.Monit. Assess.,2015,187(6):1-9
10. [10]
  CAO Jun, ZHAO Li-Jiao, ZHONG Ru-Gang. Food Science,2012,33(7):304-309 曹珺, 赵丽娇, 钟儒刚.食品科学,2012,33(7):304-309
11. [11]
  Eljan K, Marco C, Fran G. Albanian J. Agr. Sci.,2015,14(2):169-175
12. [12]
  HUANG Xiao-Chun, LIU Chang-Hong, ZHANG Jun, DONG Yong-Xiu, LIU Wen-Hua, ZHAO Qiu-Xiang, LI Xi-Kun. Rock and Mineral Analysis,2013,32(03):415-419 黄晓纯, 刘昌弘, 张军, 董泳秀, 刘文华, 赵秋香, 李锡坤. 岩矿测试,2013,32(03):415-419
13. [13]
  CHEN Qi-Meng, DU Min, HAO Zhong-Qi, ZOU Xiao-Heng, LI Jia-Ming, YI Rong-Xing, LI Xiang-You, LU Yong-Feng, ZENG Xiao-Yan. Spectroscopy and Spectral Analysis,2016,36(5):1473-1477 陈启蒙, 杜敏, 郝中骐, 邹孝恒, 李嘉铭, 易荣兴, 李祥友, 陆永枫, 曾晓雁.光谱学与光谱分析,2016,36(5):1473-1477
14. [14]
  Wang Z, Yuan T B, Hou Z Y, Zhou W D, Lu J D, Ding H B, Zeng X Y. Front. Phys.,2014,9(4):419-438
15. [15]
  SHAO Yan, ZHANG Yan-Bo, GAO Xun, DU Chuang, LIN Jing-Quan. Spectroscopy and Spectral Analysis,2013,33(10):2593-2598 邵妍, 张艳波, 高勋, 杜闯, 林景全.光谱学与光谱分析,2013,33(10):2593-2598
16. [16]
  Ho S K. Appl. Spectros.,2015,69(2):269-276
17. [17]
  Sun L X,Yu H B, Cong Z B, Xin Y,Li Y,Qi L F. Spectrochim. Acta B,2015,112:40-48
18. [18]
  Wan X, Wang P. OPTIK-Inter. J. Light and Electron. Optics,2015,126(19):1930-1934
19. [19]
  WAN Xiong, WANG Jian-Yu, YE Jian-Hua, WANG Peng, ZHANG Zhi-Min. Spectroscopy and Spectral Analysis,2013,33(01):206-209 万雄, 王建宇, 叶健华, 王鹏, 张志敏. 光谱学与光谱分析,2013,33(1):206-209
20. [20]
  Zhang T L, Wu S, Dong J, Wei J,Wang K, Tang H S, Yang X F, Li H. J. Anal. Atomic Spectrom.,2014,30(2):368-374
21. [21]
  Liu S Q, Yang X G. J. Anal. Atomic Spectrom.,2015,30(2):453-458
22. [22]
  Silvestre D M, de Oliveir Leme F, Nomura C S, do Nascimento A N. Microchem. J.,2016,126:545-550
23. [23]
  HE Xiu-Wen, CHEN Tian-Bing, YAO Ming-Yin, ZHOU Hua-Mao, HU Hui-Qin, WANG Cai-Hong, LIU Mu-Hua. Chinese J. Anal. Chem.,2016,44(1):68-73 何秀文, 陈添兵, 姚明印, 周华茂, 胡慧琴, 王彩虹, 刘木华. 分析化学,2016,44(1):68-73
24. [24]
  Sankaran S, Ehsani R, Morgan K T. Appl. Spectros.,2015,69(8):913-915
25. [25]
  CHEN Tian-Bing, LIU Mu-Hua, HUANG Lin, ZHOU Hua-Mao, WANG Cai-Hong, YANG Hui, HU Hui-Qin, YAO Ming-Yin. Chinese J. Anal. Chem.,2016,44(7):1029-1034 陈添兵, 刘木华, 黄林, 周华茂, 王彩虹, 杨晖, 胡慧琴, 姚明印. 分析化学,2016,44(7):1029-1034
26. [26]
  GB2762-2012, Maximum Levels of Contaminats in Foods, National Standars of the People's Republic of China食品中污染物限量.中华人民共和国国家标准. GB2762-2012
27. [27]
  XU Hui-Rong, WANG Hui-Jun, HUANG Kang, YING Yi-Bin, YANG Cheng, QIAN Hao, HU Jun. Spectroscopy and Spectral Analysis,2008,28(11):2523-2526 徐惠荣, 汪辉君, 黄康, 应义斌, 杨诚, 钱豪, 胡俊. 光谱学与光谱分析,2008,28(11):2523-2526

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激光诱导击穿光谱检测青菜中镉元素的多变量筛选研究

English

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

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通讯作者: 陈斌, bchen63@163.com

中国化学会秘书处

激光诱导击穿光谱检测青菜中镉元素的多变量筛选研究

English

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

CCS Chemistry：嵌段共聚物自组装成 “三明治”二维有机材料，实现纳米级压力传感功能

CCS Chemistry：颜徐州、鲍哲南： 你的皮肤可能是一片SPMs

CCS Chemistry：工作高效不疲劳，只须让催化剂动起来

CCS Chemistry：静电组装导向聚合- 聚电解质纳米凝胶量化制备新策略

CCS Chemistry：金黄色葡萄球菌醛基脱氢酶是如何识别特异性底物的？

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