光谱学与光谱分析 |
|
|
|
|
|
Quantitative Analysis of Heave Mental Ion Based on Portable NIR Spectrometer |
ZHANG Yi-ting1, WANG Cui-cui1, FAN Meng-li1, CAI Wen-sheng1, SHAO Xue-guang1,2* |
1. Research Center for Analytical Sciences, College of Chemistry, Nankai University, Tianjin Key Laboratory of Biosensing and Molecular Recognition, Tianjin 300071, China 2. College of Chemistry and Environmental Science, Kashgar University, Kashgar 844000, China |
|
|
Abstract By virtue of the characters of rapid analysis and simple pretreatment, near infrared reflectance spectroscopy technique is widely used in agriculture, medicine, environment, petrochemical and other fields. To satisfy the rapid on-site identification and analysis, portable near-infrared spectrometers have gained more and more attention. Because near infrared reflectance spectroscopy technique also is a green tool for multi-component analysis, the paper aims at investigating the feasibility for simultaneous quantitative analysis of various heavy metal ions in dilute solution using portable near infrared spectrometer. First, amino modified polymerized starch was used as adsorbent to enrich nickel ions and copper ions in diluted solution. Then, the diffuse reflectance spectra of amino modified polymerized starch samples were measured directly by portable near-infrared spectrometer. Furthermore, with the help of spectral preprocessing methods and partial least-squares regression, quantitative models were built from the near infrared diffuse reflectance spectra of amino modified polymerized starch enriched with heavy metal ions and reference concentrations. At last, the stability of the models was proved through cross validation and external validation. The results show that nickel ions and copper ions in diluted solution can be efficiently enriched by amino modified polymerized starch in the presence of other interfering ions. The adsorption rates for nickel ions and copper ions are 99.5% and 99.8%, respectively. Two robust models can be achieved after spectra processing and partial least squares regression. The spectra processing methods contains Continuous wavelet transform and multiplicative scatter correction combined with Savitzky-Golay. The obtained corresponding correlation coefficients of the two robust models are 0.981 9 and 0.965 4, respectively. Thus, simultaneous quantitative analysis of nickel ions and copper ions in the mixed diluted solution was achieved, and the detectable concentrations of nickel ions and copper ions are both as low as 3.0 mg·L-1. The method not only improves the sensitivity of near infrared reflectance spectroscopy technique, but also demonstrates the feasibility for simultaneous quantitative determination of various heavy metal ions by using portable near infrared spectrometer. Moreover, the method may be a useful exploration to further broaden the application of near infrared reflectance spectroscopy technique.
|
Received: 2015-12-03
Accepted: 2016-04-19
|
|
Corresponding Authors:
SHAO Xue-guang
E-mail: xshao@nankai.edu.cn
|
|
[1] Kwon T N, Jeon C. J. Ind. Eng. Chem., 2013, 19: 68. [2] Tuzen M, Soylak M, Elci L. Anal. Chim. Acta, 2005, 548(1): 101. [3] Freitas M C, Pacheco A M G, Dionísio I, et al. Nucl. Instrum. Meth. A, 2006, 564: 733. [4] Cerutti S, Martine L D, Wuilloud R G. Appl. Spectr. Rev., 2005, 40: 71. [5] Chen G P, Mei Y, Tao W, et al. Anal. Chim. Acta, 2010, 670: 39. [6] SHAO Xue-guang, NING Yu, LIU Feng-xia, et al(邵学广, 宁 宇, 刘凤霞, 等). Acta Chimica Sinica(化学学报), 2012, 70: 2109. [7] Moros J, Garrigues M, de la Guardia M. TrAC Trends Anal. Chem., 2010, 29: 578. [8] Moros J, Garrigues S. TrAC Trends Anal. Chem., 2010, 29: 578. [9] Macho S, Larrechi M S. Trends Anal. Chem., 2002, 21: 799. [10] Feng Y C, Zhang X B, Hu C Q. J. Pharm. Biomed. Anal., 2010, 51: 12. [11] Kooistra L, Wehrens R, Leuven R S E W, et al. Anal. Chim. Acta, 2001, 446: 97. [12] Chen G P, Mei Y, Tao W, et al. Anal. Chim. Acta, 2010, 670: 39. [13] Huang Z X, Tao W, Fang J, et al. Chemom. Intell. Lab. Syst., 2009, 98: 195. [14] Li J H, Zhang Y, Cai W S, et al. Talanta, 2011, 84: 679. [15] Sheng N, Cai W S, Shao X G. Talanta, 2009, 79: 339. [16] LIN Mei-ying, SHANG Xiao-qin, LI Shu-yan, et al(林梅莹, 尚小琴, 李淑妍, 等). Chem. Ind. Eng. Pro. (化工进展), 2011, 30: 854. [17] Forouzangohar M, Cozzolino D, Kookana R S, et al. Environ. Sci. Technol., 2009, 43: 4049. |
[1] |
GAO Feng1, 2, XING Ya-ge3, 4, LUO Hua-ping1, 2, ZHANG Yuan-hua3, 4, GUO Ling3, 4*. Nondestructive Identification of Apricot Varieties Based on Visible/Near Infrared Spectroscopy and Chemometrics Methods[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2024, 44(01): 44-51. |
[2] |
CHU Bing-quan1, 2, LI Cheng-feng1, DING Li3, GUO Zheng-yan1, WANG Shi-yu1, SUN Wei-jie1, JIN Wei-yi1, HE Yong2*. Nondestructive and Rapid Determination of Carbohydrate and Protein in T. obliquus Based on Hyperspectral Imaging Technology[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(12): 3732-3741. |
[3] |
LIU Hao-dong1, 2, JIANG Xi-quan1, 2, NIU Hao1, 2, LIU Yu-bo1, LI Hui2, LIU Yuan2, Wei Zhang2, LI Lu-yan1, CHEN Ting1,ZHAO Yan-jie1*,NI Jia-sheng2*. Quantitative Analysis of Ethanol Based on Laser Raman Spectroscopy Normalization Method[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(12): 3820-3825. |
[4] |
ZHANG Shu-fang1, LEI Lei2, LEI Shun-xin2, TAN Xue-cai1, LIU Shao-gang1, YAN Jun1*. Traceability of Geographical Origin of Jasmine Based on Near
Infrared Diffuse Reflectance Spectroscopy[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(11): 3389-3395. |
[5] |
LIN Hong-jian1, ZHAI Juan1*, LAI Wan-chang1, ZENG Chen-hao1, 2, ZHAO Zi-qi1, SHI Jie1, ZHOU Jin-ge1. Determination of Mn, Co, Ni in Ternary Cathode Materials With
Homologous Correction EDXRF Analysis[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(11): 3436-3444. |
[6] |
HUANG Li, MA Rui-jun*, CHEN Yu*, CAI Xiang, YAN Zhen-feng, TANG Hao, LI Yan-fen. Experimental Study on Rapid Detection of Various Organophosphorus Pesticides in Water by UV-Vis Spectroscopy and Parallel Factor Analysis[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(11): 3452-3460. |
[7] |
LI Zhong-bing1, 2, JIANG Chuan-dong2, LIANG Hai-bo3, DUAN Hong-ming2, PANG Wei2. Rough and Fine Selection Strategy Binary Gray Wolf Optimization
Algorithm for Infrared Spectral Feature Selection[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(10): 3067-3074. |
[8] |
LIU Shu1, JIN Yue1, 2, SU Piao1, 2, MIN Hong1, AN Ya-rui2, WU Xiao-hong1*. Determination of Calcium, Magnesium, Aluminium and Silicon Content in Iron Ore Using Laser-Induced Breakdown Spectroscopy Assisted by Variable Importance-Back Propagation Artificial Neural Networks[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(10): 3132-3142. |
[9] |
KONG De-ming1, LIU Ya-ru1, DU Ya-xin2, CUI Yao-yao2. Oil Film Thickness Detection Based on IRF-IVSO Wavelength Optimization Combined With LIF Technology[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(09): 2811-2817. |
[10] |
ZHAO Yu-wen1, ZHANG Ze-shuai1, ZHU Xiao-ying1, WANG Hai-xia1, 2*, LI Zheng1, 2, LU Hong-wei3, XI Meng3. Application Strategies of Surface-Enhanced Raman Spectroscopy in Simultaneous Detection of Multiple Pathogens[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(07): 2012-2018. |
[11] |
ZHANG Jing, GUO Zhen, WANG Si-hua, YUE Ming-hui, ZHANG Shan-shan, PENG Hui-hui, YIN Xiang, DU Juan*, MA Cheng-ye*. Comparison of Methods for Water Content in Rice by Portable Near-Infrared and Visible Light Spectrometers[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(07): 2059-2066. |
[12] |
CHENG Xiao-xiang1, WU Na2, LIU Wei2*, WANG Ke-qing2, LI Chen-yuan1, CHEN Kun-long1, LI Yan-xiang1*. Research on Quantitative Model of Corrosion Products of Iron Artefacts Based on Raman Spectroscopic Imaging[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(07): 2166-2173. |
[13] |
HU Shuang1, LIU Cui-mei2*, XU Lin3, JIA Wei2, HUA Zhen-dong2. Rapid Qualitative Analysis of Synthetic Cathinones by Raman
Spectroscopy[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(06): 1821-1828. |
[14] |
WANG Dong1, 2, FENG Hai-zhi3, LI Long3, HAN Ping1, 2*. Compare of the Quantitative Models of SSC in Tomato by Two Types of NIR Spectrometers[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(05): 1351-1357. |
[15] |
CHEN Rui1, WANG Xue1, 2*, WANG Zi-wen1, QU Hao1, MA Tie-min1, CHEN Zheng-guang1, GAO Rui3. Wavelength Selection Method of Near-Infrared Spectrum Based on
Random Forest Feature Importance and Interval Partial
Least Square Method[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(04): 1043-1050. |
|
|
|
|