光谱学与光谱分析 |
|
|
|
|
|
Research on Rapid and Quantitative Detection Method for Organophosphorus Pesticide Residue |
SUN Yuan-xin1, CHEN Bing-tai2, YI Sen2, SUN Ming2* |
1. College of Food Science and Nutritional Engineering, China Agricultural University, Beijing 100083, China 2. Key Laboratory of Modern Precision Agriculture System Integration Research, Ministry of Education, China Agricultural University, Beijing 100083, China |
|
|
Abstract The methods of physical-chemical inspection is adopted in the traditional pesticide residue detection, which require a lot of pretreatment processes,are time-consuming and complicated. In the present study, the authors take chlorpyrifos applied widely in the present agricultural field as the research object and propose a rapid and quantitative detection method for organophosphorus pesticide residues. At first, according to the chemical characteristics of chlorpyrifos and comprehensive chromogenic effect of several colorimetric reagents and secondary pollution, the pretreatment of the scheme of chromogenic reaction of chlorpyrifos with resorcin in a weak alkaline environment was determined. Secondly, by analyzing Uv-Vis spectrum data of chlorpyrifos samples whose content were between 0.5 and 400 mg·kg-1, it was confirmed that the characteristic information after the color reaction mainly was concentrated among 360~400 nm. Thirdly, the full spectrum forecasting model was established based on the partial least squares, whose correlation coefficient of calibration was 0.999 6, correlation coefficient of prediction reached 0.995 6, standard deviation of calibration (RMSEC) was 2.814 7 mg·kg-1, and standard deviation of verification (RMSEP) was 8.012 4 mg·kg-1. Fourthly, the wavelengths whose center wavelength is 400 nm was extracted as characteristic region to build a forecasting model, whose correlation coefficient of calibration was 0.999 6, correlation coefficient of prediction reached 0.999 3, standard deviation of calibration (RMSEC) was 2.566 7 mg·kg-1, standard deviation of verification (RMSEP) was 4.886 6 mg·kg-1, respectively. At last, by analyzing the near infrared spectrum data of chlorpyrifos samples with contents between 0.5 and 16 mg·kg-1, the authors found that although the characteristics of the chromogenic functional group are not obvious, the change of absorption peaks of resorcin itself in the neighborhood of 5 200 cm-1 happens. The above-mentioned experimental results show that the proposed method is effective and feasible for rapid and quantitative detection prediction for organophosphorus pesticide residues. In the method, the information in full spectrum especially UV-Vis spectrum is strengthened by chromogenic reaction of a colorimetric reagent, which provides a new way of rapid detection of pesticide residues for agricultural products in the future.
|
Received: 2013-08-06
Accepted: 2013-12-21
|
|
Corresponding Authors:
SUN Ming
E-mail: sunming@cau.edu.cn
|
|
[1] ZHENG Jin-wu, XU Yan-hong, GONG Yang-jiao, et al(郑金武,徐燕红,龚阳娇,等). Biological Disaster Science(生物灾害科学), 2013,36(2):137. [2] Akkad R, Schwack W. Journal of Planar Chromatography-Modern TLC, 2008, 21(6): 411. [3] QIU Chao-kun, LIU Xiao-yu, REN Hong-min, et al(邱朝坤,刘晓宇,任红敏,等). Food and Machinery(食品与机械),2010,26(2):40. [4] WANG Jin-bin, TAN Fu-rong, WANG Li-gang, et al(王金斌,谭芙蓉,王利刚,等). Acta Agricultural Shanghai(上海农业学报), 2009, 25(4):131. [5] Kumar M A,Chuang R S,Thakur M S, et al. Analytica Chimica Acta, 2006, 560(1-2): 30. [6] Liu Y, Lou Y, Xu D, et al. Microchemical Journal, 2009, 93(1): 36. [7] ZHOU Xiao-fang, FANG Yan, ZHANG Peng-xiang(周小芳,方 炎,张鹏翔). Chinese Journal of Light Scattering(光散射学报), 2004,16 (1):11. [8] LIU Cui-ling, SUI Shu-xia, WU Jing-zhu, et al(刘翠玲,隋淑霞,吴静珠,等). Transactions of the Chinese Society for Agricultural Machinery(农业机械学报), 2009, 40(1): 129. [9] CHEN Jing-jing, PENG Yan-kun, LI Yong-yu, et al(陈菁菁,彭彦昆,李永玉,等). Transactions of the Chinese Society for Agricultural Engineering(农业工程学报),2010,26(S2):1. [10] CAO Bing-hua, HOU Di-bo, YAN Zhi-gang, et al(曹丙花,侯迪波,颜志刚,等). Journal of Infrared and Millimeter Waves(红外与毫米波学报),2008,27(6):429.
|
[1] |
FAN Ping-ping,LI Xue-ying,QIU Hui-min,HOU Guang-li,LIU Yan*. Spectral Analysis of Organic Carbon in Sediments of the Yellow Sea and Bohai Sea by Different Spectrometers[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2024, 44(01): 52-55. |
[2] |
YANG Chao-pu1, 2, FANG Wen-qing3*, WU Qing-feng3, LI Chun1, LI Xiao-long1. Study on Changes of Blue Light Hazard and Circadian Effect of AMOLED With Age Based on Spectral Analysis[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2024, 44(01): 36-43. |
[3] |
BAO Hao1, 2,ZHANG Yan1, 2*. Research on Spectral Feature Band Selection Model Based on Improved Harris Hawk Optimization Algorithm[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2024, 44(01): 148-157. |
[4] |
LU Wen-jing, FANG Ya-ping, LIN Tai-feng, WANG Hui-qin, ZHENG Da-wei, ZHANG Ping*. Rapid Identification of the Raman Phenotypes of Breast Cancer Cell
Derived Exosomes and the Relationship With Maternal Cells[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(12): 3840-3846. |
[5] |
LI Qi-chen1, 2, LI Min-zan1, 2*, YANG Wei2, 3, SUN Hong2, 3, ZHANG Yao1, 3. Quantitative Analysis of Water-Soluble Phosphorous Based on Raman
Spectroscopy[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(12): 3871-3876. |
[6] |
LIANG Jin-xing1, 2, 3, XIN Lei1, CHENG Jing-yao1, ZHOU Jing1, LUO Hang1, 3*. Adaptive Weighted Spectral Reconstruction Method Against
Exposure Variation[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(11): 3330-3338. |
[7] |
GUO Ge1, 3, 4, ZHANG Meng-ling3, 4, GONG Zhi-jie3, 4, ZHANG Shi-zhuang3, 4, WANG Xiao-yu2, 5, 6*, ZHOU Zhong-hua1*, YANG Yu2, 5, 6, XIE Guang-hui3, 4. Construction of Biomass Ash Content Model Based on Near-Infrared
Spectroscopy and Complex Sample Set Partitioning[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(10): 3143-3149. |
[8] |
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. |
[9] |
HUANG Chao1, 2, ZHAO Yu-hong1, ZHANG Hong-ming2*, LÜ Bo2, 3, YIN Xiang-hui1, SHEN Yong-cai4, 5, FU Jia2, LI Jian-kang2, 6. Development and Test of On-Line Spectroscopic System Based on Thermostatic Control Using STM32 Single-Chip Microcomputer[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(09): 2734-2739. |
[10] |
ZHENG Yi-xuan1, PAN Xiao-xuan2, GUO Hong1*, CHEN Kun-long1, LUO Ao-te-gen3. Application of Spectroscopic Techniques in Investigation of the Mural in Lam Rim Hall of Wudang Lamasery, China[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(09): 2849-2854. |
[11] |
WANG Jun-jie1, YUAN Xi-ping2, 3, GAN Shu1, 2*, HU Lin1, ZHAO Hai-long1. Hyperspectral Identification Method of Typical Sedimentary Rocks in Lufeng Dinosaur Valley[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(09): 2855-2861. |
[12] |
WANG Jing-yong1, XIE Sa-sa2, 3, GAI Jing-yao1*, WANG Zi-ting2, 3*. Hyperspectral Prediction Model of Chlorophyll Content in Sugarcane Leaves Under Stress of Mosaic[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(09): 2885-2893. |
[13] |
CHENG Fang-beibei1, 2, GAN Ting-ting1, 3*, ZHAO Nan-jing1, 4*, YIN Gao-fang1, WANG Ying1, 3, FAN Meng-xi4. Rapid Detection of Heavy Metal Lead in Water Based on Enrichment by Chlorella Pyrenoidosa Combined With X-Ray Fluorescence Spectroscopy[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(08): 2500-2506. |
[14] |
LI Bin, SU Cheng-tao, YIN Hai, LIU Yan-de*. Hyperspectral Imaging Technology Combined With Machine Learning for Detection of Moldy Rice[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(08): 2391-2396. |
[15] |
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. |
|
|
|
|