| 光谱学与光谱分析 |
|
|
|
|
|
| Application of Near-Infrared Spectroscopy to Detection of Pesticide Phoxim Residues |
| SHEN Fei, YAN Zhan-ke, YE Zun-zhong*,YING Yi-bin |
| College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou 310029, China |
|
|
|
|
Abstract Near-infrared (NIR) spectroscopy technique was applied directly to the detection of pesticide phoxim residues. A sample pretreatment method was introduced. Samples were mixed with silica gel. Silica gel as a sorbent was employed to extract and enrich the low-concentration samples. Subsequently, diffuse reflection spectrum was measured on silica gel. Calibration models were developed using partial least square regression (PLSR) algorithm. Leave-one-out cross-validation was used to evaluate and compare the models. Two experiments were carried out, and the results show that 21 samples with the concentration gradient of 0.5 mg·L-1 exhibited a high correlation coefficient of cross-validation of 0.958, and a root mean square error of cross validation (RMSECV) of 0.872 mg·L-1, while 41 samples with the concentration gradient of 0.25 mg·L-1 gave a correlation coefficient of cross-validation of 0.924 and RMSECV of 1.15 mg·L-1. It is indicated that with the reduction in concentration gradient, the prediction capacity of models dropped, but there still existed a high correlation coefficient with the concentration of phoxim in the samples. The experiments proved that the sample pretreatment method with the introduction of silica gel as an absorber to enrich low concentration analyte was effective. The method was able to lower the detection limit of NIR. The developed technique has a potential application in low-concentration sample detection by NIR spectroscopy, such as pesticide residues.
|
|
Received: 2008-03-28
Accepted: 2008-06-29
|
|
|
|
Corresponding Authors:
YE Zun-zhong
E-mail: zzye@zju.edu.cn
|
|
[1] FU Xia-ping, YING Yi-bin, LU Hui-shan, et al(傅霞萍，应义斌，陆辉山，等). Spectroscopy and Spectral Analysis(光谱学与光谱分析), 2007, 27(5): 911.
[2] Albuquerque J S, Pimentel M F, Silva V L. Anal. Chem., 2005, 77(1): 72.
[3] Lima K M G, Raimundo I M, Pimentel M F. Sens. Actuators A: Phys., 2007, 125(1): 229.
[4] Pearson T C, Wicklow D T, Maghirang E B, et al. Transaction of American Society of Agricultural Engineering, 2001, 44(5): 1247.
[5] Ruan R, Li Y, Lin X, et al. Appl. Eng. Agric., 2002, 18(5): 549.
[6] Moros J, Llorca I, Cervera M L, et al. Anal. Chim. Acta, 2008, 613: 196.
[7] Balanco M, Castillo M, Peinado A, et al. Anal. Chim. Acta, 2007, 581: 318.
[8] Saranwong S, Kawano S. J. Near Infrared Spectrosc., 2005, 13: 169.
[9] Saranwong S, Kawano S. J. Near Infrared Spectrosc., 2007, 15: 227.
[10] Wei Xuimei, Huang Zixia, Zhang Wei, et al. Anal. Sci., 2007, 23: 853.
[11] ZHOU Xiang-yang, LIN Chun-zhong, HU Xiang-na, et al(周向阳，林纯忠，胡祥娜，等). Food Science(食品科学), 2004, 25(5): 151. |
| [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] |
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. |
| [3] |
HU Cai-ping1, HE Cheng-yu2, KONG Li-wei3, ZHU You-you3*, WU Bin4, ZHOU Hao-xiang3, SUN Jun2. Identification of Tea Based on Near-Infrared Spectra and Fuzzy Linear Discriminant QR Analysis[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(12): 3802-3805. |
| [4] |
LIU Xin-peng1, SUN Xiang-hong2, QIN Yu-hua1*, ZHANG Min1, GONG Hui-li3. Research on t-SNE Similarity Measurement Method Based on Wasserstein Divergence[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(12): 3806-3812. |
| [5] |
BAI Xue-bing1, 2, SONG Chang-ze1, ZHANG Qian-wei1, DAI Bin-xiu1, JIN Guo-jie1, 2, LIU Wen-zheng1, TAO Yong-sheng1, 2*. Rapid and Nndestructive Dagnosis Mthod for Posphate Dficiency in “Cabernet Sauvignon” Gape Laves by Vis/NIR Sectroscopy[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(12): 3719-3725. |
| [6] |
WANG Qi-biao1, HE Yu-kai1, LUO Yu-shi1, WANG Shu-jun1, XIE Bo2, DENG Chao2*, LIU Yong3, TUO Xian-guo3. Study on Analysis Method of Distiller's Grains Acidity Based on
Convolutional Neural Network and Near Infrared Spectroscopy[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(12): 3726-3731. |
| [7] |
LUO Li, WANG Jing-yi, XU Zhao-jun, NA Bin*. Geographic Origin Discrimination of Wood Using NIR Spectroscopy
Combined With Machine Learning Techniques[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(11): 3372-3379. |
| [8] |
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. |
| [9] |
YANG Qun1, 2, LING Qi-han1, WEI Yong1, NING Qiang1, 2, KONG Fa-ming1, ZHOU Yi-fan1, 2, ZHANG Hai-lin1, WANG Jie1, 2*. Non-Destructive Monitoring Model of Functional Nitrogen Content in
Citrus Leaves Based on Visible-Near Infrared Spectroscopy[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(11): 3396-3403. |
| [10] |
HUANG Meng-qiang1, KUANG Wen-jian2, 3*, LIU Xiang1, HE Liang4. Quantitative Analysis of Cotton/Polyester/Wool Blended Fiber Content by Near-Infrared Spectroscopy Based on 1D-CNN[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(11): 3565-3570. |
| [11] |
HUANG Zhao-di1, CHEN Zai-liang2, WANG Chen3, TIAN Peng2, ZHANG Hai-liang2, XIE Chao-yong2*, LIU Xue-mei4*. Comparing Different Multivariate Calibration Methods Analyses for Measurement of Soil Properties Using Visible and Short Wave-Near
Infrared Spectroscopy Combined With Machine Learning Algorithms[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(11): 3535-3540. |
| [12] |
KANG Ming-yue1, 3, WANG Cheng1, SUN Hong-yan3, LI Zuo-lin2, LUO Bin1*. Research on Internal Quality Detection Method of Cherry Tomatoes Based on Improved WOA-LSSVM[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(11): 3541-3550. |
| [13] |
HUANG Hua1, LIU Ya2, KUERBANGULI·Dulikun1, ZENG Fan-lin1, MAYIRAN·Maimaiti1, AWAGULI·Maimaiti1, MAIDINUERHAN·Aizezi1, GUO Jun-xian3*. Ensemble Learning Model Incorporating Fractional Differential and
PIMP-RF Algorithm to Predict Soluble Solids Content of Apples
During Maturing Period[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(10): 3059-3066. |
| [14] |
CHEN Jia-wei1, 2, ZHOU De-qiang1, 2*, CUI Chen-hao3, REN Zhi-jun1, ZUO Wen-juan1. Prediction Model of Farinograph Characteristics of Wheat Flour Based on Near Infrared Spectroscopy[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(10): 3089-3097. |
| [15] |
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. |
|
|
|
|
