异丙甲草胺农药残留吸收光谱检测方法研究
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摘要
除草剂可以快速、有效地进行除草,已被广泛应用,但是在除草剂使用同时也会对周围环境和农作物带来一定程度的污染,例如农业生产过程中经常发现由于除草剂使用不当而使果树中毒的现象。异丙甲草胺是一种酰胺类选择性除草剂,被广泛应用于旱地作物、蔬菜和果园、苗圃。根据相关文献报道,基于气相色谱法、气相色谱-质谱联用法和固相萃取等方法可以实现异丙甲草胺残留检测,而基于吸收光谱法对异丙甲草胺的分析未见相关文献报道,提出直接利用吸收光谱及其导数光谱分析法实现异丙甲草胺农药及其在苹果汁中的农药残留检测。首先利用分光光度计对不同浓度异丙甲草胺药液进行吸收光谱实验研究,发现在266nm处有明显吸收光谱特征峰。对农药吸收光谱进行拟合分析,得到异丙甲草胺药液浓度和吸光度之间预测模型函数方程,函数方程为y=2.147 09x+0.031 98,相关系数为0.998 5。然后利用分光光度计对苹果汁-异丙甲草胺混合溶液进行吸收光谱实验研究,相对于纯苹果汁吸收光谱,在混合溶液吸收光谱中发现266nm处为异丙甲草胺所对应的特征峰。对苹果汁中药物浓度和吸光度进行建模,模型函数为:y=0.704 9+0.826 8x,其相关系数为0.991 1。可以看出,当苹果汁中异丙甲草胺残留量很低时,其农药吸收光谱特征峰并不明显。为进一步提高检测效果,对混合溶液吸收光谱进行一阶导数处理,得到其一阶导数吸收光谱。与苹果汁吸收导数光谱相比较,苹果汁-农药混合溶液导数光谱有两个明显特征光谱峰,分别位于269和276nm处。进一步分析苹果汁-异丙甲草胺混合溶液的导数吸收光谱峰值与农药含量之间的关系,对异丙甲草胺含量与导数光谱吸光度进行函数拟合。其中269nm对应预测模型函数关系式:y=0.005 3-0.090 6x,相关系数r=0.992 5;276nm对应预测模型函数关系式为y=-0.000 769-0.302 8x,相关系数r=0.990 6。为验证由吸收光谱和其一阶导数光谱所得苹果汁中农药残留预测模型的准确性,另外配置五种浓度苹果汁-异丙甲草胺混合溶液。然后在同等条件下对其进行吸收光谱实验,将266,269和276nm处的吸光度分别代入对应模型函数可求得浓度预测值,结合已知浓度值可计算其平均回收率,其中吸收光谱266nm对应平均回收率为104.68%,导数光谱269nm对应平均回收率为104.59%,276nm对应平均回收率为105.18%。对苹果汁中异丙甲草胺检测模型进行分析,计算得到检出限(LOD)和定量限(LOQ)参数值,其中原始吸收光谱对应LOD和LOQ分别为0.014 8和0.049 2mg·mL~(-1),一阶导数光谱对应LOD和LOQ最小值分别为0.001 5和0.004 9mg·mL~(-1)。研究结果表明,采用吸收光谱方法对苹果汁中异丙甲草胺进行直接检测与分析是快速和可行有效的,而且对吸收光谱进行导数运算处理后,检测效果更优。
Herbicides have been widely used because of their rapid and effective weeding.But it will also cause a certain degree of pollution to the environment and the crops,and it is often found that the misuse of herbicides causes the fruit trees to be poisoned in the process of agricultural production.Metolachlor is a selective amide herbicide which is widely used in upland crops,vegetable crops,orchard and nursery.According to the relevant literature,the methods of Metolachlor residue detection mainly include Gas chromatography(GC),Gas chromatograph-mass spectrometry(GC-MS),Solid phase extraction(SPE)and so on.The analysis of metolachlor residues based on absorption spectroscopy has not been reported in literature.This paper presented the absorption spectroscopy and its derivative spectrometry to detect the metolachlor pesticide residues in the apple juice.First,the absorption spectrum of different concentration of metolachlor were recorded by spectrophotometer,and it was found that there was a distinct absorption spectrum peak at 266 nm.The relationship between the pesticide concentration and absorbance was obtained by fitting analysis on the absorption spectrum of metolachlor,and the function equation was deduced as y=2.147 09x+0.031 98,and the correlation coefficient was 0.998 5.Second,the absorption spectrum of mixed solution of apple juice and metolachlor were studied by spectrophotometer.Compared with the absorption spectrum of apple juice,the metolachlor characteristic peak at 266 nm was also found in the absorption spectrum of mixed solution.The model function between absorbance and metolachlor concentration in apple juice was further obtained as follows:y=0.704 9+0.826 8x,its correlation coefficient was0.991 1.It can be seen that when the residual amount of metolachlor in apple juice was very low,the absorption spectrum characteristic peak of the pesticide was not obvious.Third,in order to further improve the detection effect,the first derivative processing of the absorption spectrum was carried out,and the first order derivative absorption spectrum of the mixed solution were then obtained.Compared with the derivative absorption spectrum of apple juice,the derivative absorption spectrum of mixed solution of apple juice and metolachlor pesticide had two distinct spectral peaks,which were located at 269 and 276nm,respectively.In order to further analyze the relationship between the metolachlor content and the peak value of the derivative absorption spectrum of mixed solution,the metolachlor content and the absorbance of derivative spectrum were linearly fitted,and the corresponding prediction model in 269 nm was deduced as y=0.005 3-0.090 6x,and the correlation coefficient was r=0.992 5.The prediction model corresponding to 276 nm was deduced as y=-0.000 769-0.302 8x,and the correlation coefficient was r=0.990 6.At last,in order to verify the accuracy of the prediction model obtained from the absorption spectrum and its first derivative spectrum,five different concentrations of the mixed solution of metolachlor and apple juice were configured and tested under the same experiment condition.The absorbance value at 266,269 and 276nm were substituted into the model function respectively,and the predictive value of the metolachlor concentration can be obtained,the average recovery rate can be further calculated according to the actual metolachlor concentration.The calculation results were as follows:the average recovery rate of the absorption spectrum at 266 nm was 104.68%,and the average recovery rate of the derivative spectrum at 269 nm was104.59%,and it was 105.18% at the absorption peak of 276 nm.The limit of detection(LOD)and limit of quantification(LOQ)parameters were calculated by analyzing the detection model of metolachlor in apple juice.The LOD and LOQ of the original absorption spectrum were 0.014 8mg·mL~(-1) and 0.049 2mg·mL~(-1),respectively.And the minimum values of LOD and LOQ corresponding to the first derivative absorption spectra were 0.001 5mg·mL~(-1) and 0.004 9mg·mL~(-1),respectively.The results showed that absorption spectroscopy was fast and feasible for the detection and analysis of metolachlor residue in apple juice,and the detection effect was better after the derivative operation of absorption spectrum.
Herbicides have been widely used because of their rapid and effective weeding.But it will also cause a certain degree of pollution to the environment and the crops,and it is often found that the misuse of herbicides causes the fruit trees to be poisoned in the process of agricultural production.Metolachlor is a selective amide herbicide which is widely used in upland crops,vegetable crops,orchard and nursery.According to the relevant literature,the methods of Metolachlor residue detection mainly include Gas chromatography(GC),Gas chromatograph-mass spectrometry(GC-MS),Solid phase extraction(SPE)and so on.The analysis of metolachlor residues based on absorption spectroscopy has not been reported in literature.This paper presented the absorption spectroscopy and its derivative spectrometry to detect the metolachlor pesticide residues in the apple juice.First,the absorption spectrum of different concentration of metolachlor were recorded by spectrophotometer,and it was found that there was a distinct absorption spectrum peak at 266 nm.The relationship between the pesticide concentration and absorbance was obtained by fitting analysis on the absorption spectrum of metolachlor,and the function equation was deduced as y=2.147 09x+0.031 98,and the correlation coefficient was 0.998 5.Second,the absorption spectrum of mixed solution of apple juice and metolachlor were studied by spectrophotometer.Compared with the absorption spectrum of apple juice,the metolachlor characteristic peak at 266 nm was also found in the absorption spectrum of mixed solution.The model function between absorbance and metolachlor concentration in apple juice was further obtained as follows:y=0.704 9+0.826 8x,its correlation coefficient was0.991 1.It can be seen that when the residual amount of metolachlor in apple juice was very low,the absorption spectrum characteristic peak of the pesticide was not obvious.Third,in order to further improve the detection effect,the first derivative processing of the absorption spectrum was carried out,and the first order derivative absorption spectrum of the mixed solution were then obtained.Compared with the derivative absorption spectrum of apple juice,the derivative absorption spectrum of mixed solution of apple juice and metolachlor pesticide had two distinct spectral peaks,which were located at 269 and 276nm,respectively.In order to further analyze the relationship between the metolachlor content and the peak value of the derivative absorption spectrum of mixed solution,the metolachlor content and the absorbance of derivative spectrum were linearly fitted,and the corresponding prediction model in 269 nm was deduced as y=0.005 3-0.090 6x,and the correlation coefficient was r=0.992 5.The prediction model corresponding to 276 nm was deduced as y=-0.000 769-0.302 8x,and the correlation coefficient was r=0.990 6.At last,in order to verify the accuracy of the prediction model obtained from the absorption spectrum and its first derivative spectrum,five different concentrations of the mixed solution of metolachlor and apple juice were configured and tested under the same experiment condition.The absorbance value at 266,269 and 276nm were substituted into the model function respectively,and the predictive value of the metolachlor concentration can be obtained,the average recovery rate can be further calculated according to the actual metolachlor concentration.The calculation results were as follows:the average recovery rate of the absorption spectrum at 266 nm was 104.68%,and the average recovery rate of the derivative spectrum at 269 nm was104.59%,and it was 105.18% at the absorption peak of 276 nm.The limit of detection(LOD)and limit of quantification(LOQ)parameters were calculated by analyzing the detection model of metolachlor in apple juice.The LOD and LOQ of the original absorption spectrum were 0.014 8mg·mL~(-1) and 0.049 2mg·mL~(-1),respectively.And the minimum values of LOD and LOQ corresponding to the first derivative absorption spectra were 0.001 5mg·mL~(-1) and 0.004 9mg·mL~(-1),respectively.The results showed that absorption spectroscopy was fast and feasible for the detection and analysis of metolachlor residue in apple juice,and the detection effect was better after the derivative operation of absorption spectrum.
引文
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[15] LIU Peng-xi,WAN Xiong,ZHANG Ting-ting(刘鹏希,万雄,章婷婷).Spectroscopy and Spectral Analysis(光谱学与光谱分析),2017,37(11):3510.
[2] WANG Zheng-gui,ZHOU Li-yun,GUO Wen-shan,et al(王正贵,周立云,郭文善,等).Journal of Agro-Environment Science(农业环境科学学报),2011,30(6):1037.
[3] FU Zhi-kun(付志坤).Northern Horticulture(北方园艺),2009,5:170.
[4] HE Min,YU Ping-zhong,CHEN Li,et al(贺敏,余平中,陈莉,等).Chinese Agricultural Science Bulletin(中国农学通报),2010,26(1):248.
[5] FENG Jian-guo,FAN Teng-fei,CHEN Wei-tao,et al(冯建国,范腾飞,陈维韬,等).Modern Agrochemicals(现代农药),2012,11(6):27.
[6] YE Qian,LU Da-hai,DENG Yi-cai,et al(叶倩,路大海,邓义才,等).Chinese Journal of Pesticide Science(农药学学报),2016,18(2):268.
[7] Jonathan D Byer,John Struger,Ed Sverko,et al.Chemosphere,2011,82(8):1155.
[8] Yu Wanying,Zhang Hua,Huang Weidong,et al.Talanta,2005,65(1):172.
[9] Martha J M Wells,Daniel D Riemer,Mary C Wells-Knecht.Journal of Chromatography A,1994,659(2):337.
[10] Wen Y Z,Yuan Y L,Chen H,et al.Journal of Environmental Science&Health Part B,2010,45(3):249.
[11] Sakkas V A,Arabatzis I M,Konstantinou I K,et al.Applied Catalysis B:Environmental,2004,49(3):195.
[12] Orge C A,Pereira M F R,Faria J L.Chemical Engineering Journal,2017,318:247.
[13] LIU Shao-ping,GONG Dao-xin,HE Zong-tao,et al(刘少平,龚道新,何宗桃).Hunan Agricultural Sciences(湖南农业科学),2010,(5):88.
[14] SUN Yuan-tao,ZHANG Hong-tian(孙远涛,张洪田).Chinese Journal of Luminescence(发光学报),2015,36(3):366.
[15] LIU Peng-xi,WAN Xiong,ZHANG Ting-ting(刘鹏希,万雄,章婷婷).Spectroscopy and Spectral Analysis(光谱学与光谱分析),2017,37(11):3510.