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样品制备方法对LIBS检测饲料中铜含量的影响
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  • 英文篇名:Study on the influence of sample preparation method on LIBS determination of copper content in feed
  • 作者:刘珊珊 ; 张俊 ; 林思寒 ; 刘木华 ; 赖曲芳 ; 黎静 ; 张文功
  • 英文作者:LIU Shan-shan;ZHANG Jun;LIN Si-han;LIU Mu-hua;LAI Qu-fang;LI Jing;ZHANG Wen-gong;School of Engineering,Jiangxi Agricultural University;Jiangxi Key Laboratory of Modern Agricultural Equipment;College of Animal Science and Technology,Jiangxi Agricultural University;Collaborative Innovation Center of Postharvest Key Technology and Quality Safety of Fruits and Vegetables in Jiangxi Province;
  • 关键词:激光诱导击穿光谱 ; 猪饲料 ; 铜元素 ; 样品制备
  • 英文关键词:Laser Induced Breakdown Spectroscopy(LIBS);;Pig feed;;Copper;;Sample preparation
  • 中文刊名:FXSY
  • 英文刊名:Chinese Journal of Analysis Laboratory
  • 机构:江西农业大学工学院;江西省现代农业装备重点实验室;江西农业大学动物科学技术学院;江西省果蔬采后处理关键技术与质量安全协同创新中心;
  • 出版日期:2018-02-01 17:59
  • 出版单位:分析试验室
  • 年:2018
  • 期:v.37
  • 基金:国家自然科学基金项目(31460419);; 江西省教育厅科学技术研究项目(GJJ160369);; 赣鄱“555”英才资助
  • 语种:中文;
  • 页:FXSY201802005
  • 页数:6
  • CN:02
  • ISSN:11-2017/TF
  • 分类号:28-33
摘要
通过利用共轴双脉冲激光诱导击穿光谱(DP-LIBS)检测猪饲料中的重金属铜(Cu)含量,对硫酸铜固体混合添加法和硫酸铜溶液混合添加法制得的含铜猪饲料样品进行对比研究。通过对比,硫酸铜固体混合添加法和硫酸铜溶液混合添加法LIBS线性拟合相关系数分别为0.9718,0.9946,同浓度相对标准偏差(RSD)均低于3%、预测平均相对误差分别为8.5%,6.1%。硫酸铜溶液混合添加法制得样品较硫酸铜固体混合添加法检测效果较好,但是硫酸铜固体混合方法就能满足检测要求,可以将其应用于实际生产的在线检测中。
        This study used coaxial double pulse laser induced breakdown spectroscopy(DP-LIBS) to detect copper content in pig feed and compared Cu content in different pig feeds prepared by copper sulfate solid mixed method and copper sulfate solution mixed method.By contrast,linear fitting correlation coefficient for copper sulfate solid mixed method and copper sulfate solution mixed method were 0.9718 and 0.9946,respectively and the relative standard deviations(Relative Standard,Deviation,RSD) were lower than 3%,and the average relative error of prediction were 8.5% and 6.1%,respectively.Copper sulfate solution mixed samples had a better detection results than copper sulfate solid mixed ones,which could also meet the test requirements,and can be applied to the online detection of actual production.
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