大白菜中马拉硫磷农药的表面增强拉曼光谱快速检测
为了检测大白菜中马拉硫磷农药残留,该文采用表面增强拉曼光谱技术结合化学计量学方法建立马拉硫磷残留的快速检测模型。采用硫酸镁、N-丙基乙二胺、石墨化炭黑和C18去除大白菜中蛋白质、脂肪、碳水化合物等物质的影响。利用不同预处理方法对原始光谱信号进行预处理,建立大白菜中马拉硫磷残留的偏最小二乘模型。研究发现,大白菜中马拉硫磷的检测浓度达到1.082 mg/L以下;归一化预处理后建立的模型预测性能最好。配制5个未知浓度样本验证模型的准确度,预测值与真实值相对误差的绝对值为0.70%~9.84%,预测回收率为99.30%~109.84%;配对t检验的结果表明样本的预测值与真实值之间无明显差异,说明模型是...
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Published in | 农业工程学报 Vol. 32; no. 6; pp. 296 - 301 |
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Main Author | |
Format | Journal Article |
Language | Chinese |
Published |
江苏大学现代农业装备与技术教育部重点实验室,镇江212013
2016
江西农业大学工学院生物光电及应用重点实验室,南昌330045%江西农业大学工学院生物光电及应用重点实验室,南昌,330045%江苏大学现代农业装备与技术教育部重点实验室,镇江,212013 |
Subjects | |
Online Access | Get full text |
ISSN | 1002-6819 |
DOI | 10.11975/j.issn.1002-6819.2016.06.041 |
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Summary: | 为了检测大白菜中马拉硫磷农药残留,该文采用表面增强拉曼光谱技术结合化学计量学方法建立马拉硫磷残留的快速检测模型。采用硫酸镁、N-丙基乙二胺、石墨化炭黑和C18去除大白菜中蛋白质、脂肪、碳水化合物等物质的影响。利用不同预处理方法对原始光谱信号进行预处理,建立大白菜中马拉硫磷残留的偏最小二乘模型。研究发现,大白菜中马拉硫磷的检测浓度达到1.082 mg/L以下;归一化预处理后建立的模型预测性能最好。配制5个未知浓度样本验证模型的准确度,预测值与真实值相对误差的绝对值为0.70%~9.84%,预测回收率为99.30%~109.84%;配对t检验的结果表明样本的预测值与真实值之间无明显差异,说明模型是准确可靠的。结果表明,SERS(surface-enhanced Raman spectroscopy)方法可以实现大白菜中马拉硫磷残留的快速检测。 |
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Bibliography: | 11-2047/S spectrum analysis; pesticides; measurements; surface-enhanced Raman spectroscopy; chinese cabbage; malathion; partial least squares(PLS); rapid detection The traditional pesticide residues detection methods had the disadvantages of complex sample preparation,expensive apparatus and high cost. For developing a rapid analysis detection method of pesticide residues, we investigated a surface-enhanced Raman spectroscopy(SERS) method coupled with colloidal gold for detection and characterization malathion residues in Chinese cabbage. Chemometric method was used to establish a rapid detection model of malathion pesticide residues in Chinese cabbage. A 200 mg/L standard solution was prepared by dissolving malathion power in acetonitrile. The standard solution was serially diluted with ultrapure water to prepare working solutions of 100, 50, 20, 15,10, 5, 2, 1 and 0.5 mg/L. Fresh Chinese cabbages were collected from the agronomy experimental base of Jiangxi Agricultural University in June 2015. The Chinese ca |
ISSN: | 1002-6819 |
DOI: | 10.11975/j.issn.1002-6819.2016.06.041 |