| 光谱学与光谱分析 |
|
|
|
|
|
| Study on Fluorescence Spectra of Chlorothalonil Residues and the Interaction between Chlorothalonil and Chinese Herbal Medicines |
| JI Ren-dong1,2, ZHAO Zhi-min1*, CHEN Meng-lan1, WANG Le-xin1, ZHU Xing-yue1 |
1. Nanjing University of Aeronautics and Astronautics, Nanjing 210016, China
2. Huaiyin Institute of Technology, Huaian 223003, China |
|
|
|
|
Abstract The fluorescence spectrum was studied for the chlorothalonil(0.292 8 mg·mL-1) using spectrofluorophotometer. The experiment results showed that the characteristic peaks (352 and 366 nm) are found in the spectrum of chlorothalonil standard solution when the excitation wavelength is 320 nm. And it was found that the shoulder peak gradually disappeared at 366 nm, while the fluorescence peak is stable at 352 nm with the decline of the solution concentration. The exponential functional relationship between the concentration of chlorothalonil and fluorescence intensity at 352 nm was obtained, and its correlation coefficient is 0.999. The experimental results are consistent with the theoretical formula about fluorescence intensity and concentration. The prediction model functions were also obtained through the liner fitting to the chlorothalonil solution of low concentration, and the correlation coefficient is 0.995. The limit of detection (LOD) is 0.018 8 μg·mL-1, the limit of quantification (LOQ) is 0.062 7 μg·mL-1, and the linear range is 0.062 7～28.45 μg·mL-1. And fluorescence spectra were studied for the mixed system of astragalus, medlar and chlorothalonil. It was found that the fluorescence intensity of chlorothalonil solution is all declined with the addition of two kinds of Chinese Herbal Medicines, which indicates that there is an interaction between them. The decay rate of fluorescence intensity was obtained which is 88.5% and 99.7%, respectively. Then the model functions were established between fluorescence intensity and the volume of addition, and the correlation coefficient is 0.994 and 0.997, respectively. This study provides the experimental foundation for the detection of chlorothalonil residues using fluorescence spectrum. It is shown that it is possible to detect pesticide residues of chlorothalonil using fluorescence spectra directly, and the relevant parameter value satisfied the requirement of testing standard. Therefore there is an important value for further detecting the pesticide residues in fruit juice using fluorescence spectrum. It was also found that the fluorescence intensity of chlorothalonil is decreased with the addition of astragalus or medlar, which provides the new research approach to studying the pesticide degradation using medicinal and edible Chinese Herbal Medicines.
|
|
Received: 2014-01-01
Accepted: 2014-05-08
|
|
|
|
Corresponding Authors:
ZHAO Zhi-min
E-mail: nuaazhzhm@126.com
|
|
[1] Anthony Arvanites C, Boerth D W. J. Mol. Model.，2001, 7(7): 245.
[2] Singler W V, Turco R F. Appl. Soil Ecol.，2002, 21(2): 107.
[3] Kazos E A, Nanos C G, Stalikas C D, et al. Chemosphere, 2008, 72(10): 1413.
[4] Chaves A, Shea D, Danehower D. Chemosphere, 2008, 71(4): 629.
[5] WANG Ling-ling, HOU Yu-ze, ZHI Ai-min, et al. Food Science，2013, 34(7): 326.
[6] The Ministry of Health and the Ministry of Agriculture of the People’s Republic of China, the China National Standardization Management Committee. Maximum Residue Limits for 12 Pesticides in Food. Beijing: Standards Press of China, 2010.
[7] Ministry of Health of the People’s Republic of China, the China National Standardization Management Committee. Drinking Water Sanitary Standard. Beijing: Standards Press of China, 2006.
[8] Aprea C, Centi L, Lunghini L，et al. Journal of Chromatography B, 2002, 778(1-2): 131.
[9] WANG Le-xin, ZHAO Zhi-min, CHEN Hui, et al. Spectroscopy and Spectral Analysis, 2009，29(5)：1336.
[10] JI Ren-dong, ZHAO Zhi-min, ZHANG Lin, et al. Spectroscopy and Spectral Analysis, 2013，33(3)：668.
[11] LIU Xin-she, JIAN Zai-hai, WANG Ji-qing, et al. Transactions of the CSAE，2011, 27(1): 355.
[12] Chen J Y，Lin Y J，Kuo W C. Journal of Food Engineering, 2013, 114(3):404.
[13] WANG Hong-qing, ZHONG Ai-guo, LI Shan. Environmental Chemistry，2000，19(1)：84.
[14] LIU Wan-cang, SUN Lei, QIAO Shan-yi, et al. Journal of International Pharmaceutical Research，2011, 38(3): 229.
[15] XU Jin-gou, WANG Zun-ben. Fluorescence analysis. Beijing: Science Press, 2006. |
| [1] |
LEI Hong-jun1, YANG Guang1, PAN Hong-wei1*, WANG Yi-fei1, YI Jun2, WANG Ke-ke2, WANG Guo-hao2, TONG Wen-bin1, SHI Li-li1. Influence of Hydrochemical Ions on Three-Dimensional Fluorescence
Spectrum of Dissolved Organic Matter in the Water Environment
and the Proposed Classification Pretreatment Method[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2024, 44(01): 134-140. |
| [2] |
GU Yi-lu1, 2，PEI Jing-cheng1, 2*，ZHANG Yu-hui1, 2，YIN Xi-yan1, 2，YU Min-da1, 2, LAI Xiao-jing1, 2. Gemological and Spectral Characterization of Yellowish Green Apatite From Mexico[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2024, 44(01): 181-187. |
| [3] |
HAN Xue1, 2, LIU Hai1, 2, LIU Jia-wei3, WU Ming-kai1, 2*. Rapid Identification of Inorganic Elements in Understory Soils in
Different Regions of Guizhou Province by X-Ray
Fluorescence Spectrometry[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2024, 44(01): 225-229. |
| [4] |
WANG Hong-jian1, YU Hai-ye1, GAO Shan-yun1, LI Jin-quan1, LIU Guo-hong1, YU Yue1, LI Xiao-kai1, ZHANG Lei1, ZHANG Xin1, LU Ri-feng2, SUI Yuan-yuan1*. A Model for Predicting Early Spot Disease of Maize Based on Fluorescence Spectral Analysis[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(12): 3710-3718. |
| [5] |
CHENG Hui-zhu1, 2, YANG Wan-qi1, 2, LI Fu-sheng1, 2*, MA Qian1, 2, ZHAO Yan-chun1, 2. Genetic Algorithm Optimized BP Neural Network for Quantitative
Analysis of Soil Heavy Metals in XRF[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(12): 3742-3746. |
| [6] |
SONG Yi-ming1, 2, SHEN Jian1, 2, LIU Chuan-yang1, 2, XIONG Qiu-ran1, 2, CHENG Cheng1, 2, CHAI Yi-di2, WANG Shi-feng2，WU Jing1, 2*. Fluorescence Quantum Yield and Fluorescence Lifetime of Indole, 3-Methylindole and L-Tryptophan[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(12): 3758-3762. |
| [7] |
YANG Ke-li1, 2, PENG Jiao-yu1, 2, DONG Ya-ping1, 2*, LIU Xin1, 2, LI Wu1, 3, LIU Hai-ning1, 3. Spectroscopic Characterization of Dissolved Organic Matter Isolated From Solar Pond[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(12): 3775-3780. |
| [8] |
LI Xiao-li1, WANG Yi-min2*, DENG Sai-wen2, WANG Yi-ya2, LI Song2, BAI Jin-feng1. Application of X-Ray Fluorescence Spectrometry in Geological and
Mineral Analysis for 60 Years[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(10): 2989-2998. |
| [9] |
XUE Fang-jia, YU Jie*, YIN Hang, XIA Qi-yu, SHI Jie-gen, HOU Di-bo, HUANG Ping-jie, ZHANG Guang-xin. A Time Series Double Threshold Method for Pollution Events Detection in Drinking Water Using Three-Dimensional Fluorescence Spectroscopy[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(10): 3081-3088. |
| [10] |
SUN Cheng-yu1, JIAO Long1*, YAN Na-ying1, YAN Chun-hua1, QU Le2, ZHANG Sheng-rui3, MA Ling1. Identification of Salvia Miltiorrhiza From Different Origins by Laser
Induced Breakdown Spectroscopy Combined with Artificial Neural
Network[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(10): 3098-3104. |
| [11] |
MA Qian1, 2, YANG Wan-qi1, 2, LI Fu-sheng1, 2*, CHENG Hui-zhu1, 2, ZHAO Yan-chun1, 2. Research on Classification of Heavy Metal Pb in Honeysuckle Based on XRF and Transfer Learning[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(09): 2729-2733. |
| [12] |
JIA Yu-ge1, YANG Ming-xing1, 2*, YOU Bo-ya1, YU Ke-ye1. Gemological and Spectroscopic Identification Characteristics of Frozen Jelly-Filled Turquoise and Its Raw Material[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(09): 2974-2982. |
| [13] |
YANG Xin1, 2, XIA Min1, 2, YE Yin1, 2*, WANG Jing1, 2. Spatiotemporal Distribution Characteristics of Dissolved Organic Matter Spectrum in the Agricultural Watershed of Dianbu River[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(09): 2983-2988. |
| [14] |
CHEN Wen-jing, XU Nuo, JIAO Zhao-hang, YOU Jia-hua, WANG He, QI Dong-li, FENG Yu*. Study on the Diagnosis of Breast Cancer by Fluorescence Spectrometry Based on Machine Learning[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(08): 2407-2412. |
| [15] |
ZHU Yan-ping1, CUI Chuan-jin1*, CHENG Peng-fei1, 2, PAN Jin-yan1, SU Hao1, 2, ZHANG Yi1. Measurement of Oil Pollutants by Three-Dimensional Fluorescence
Spectroscopy Combined With BP Neural Network and SWATLD[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(08): 2467-2475. |
|
|
|
|
