摘要
为了高效监控蔬菜中农药残留情况,利用荧光光谱技术检测白菜中吡虫啉农药残留量。首先通过三维荧光光谱确定400nm为吡虫啉的最佳激发波长;其次通过分析6种预处理算法和2种降维算法,分别选出多元散射校正(Multiple scattering calibration,MSC)和无信息变量消除(Uninformative variable elimination,UVE)作为最佳的预处理与波长选择方法;宽度学习系统(Broad learning system,BLS)用于荧光光谱建模,同时与偏最小二乘回归(Partial least squares regression,PLSR)、支持向量机(Support vector machine,SVM)和深度极限学习机(Deep extreme learning machines,DELM)等经典模型进行比较。结果显示BLS模型获得了最佳吡虫啉含量预测效果,测试集决定系数R^(2)_(p)达0.949,均方根误差(Root mean square error,RMSE)达0.347 mg/kg。表明了荧光光谱技术结合宽度学习预测农药残留量的可行性,可以为在线检测农药残留量系统的开发提供理论依据。
In order to efficiently monitor the pesticide residues in vegetables,a detection method of pesticide residue content of imidacloprid in cabbage on fluorescence spectroscopy was proposed.Firstly,400nm was determined of as the optimal excitation wavelength of imidacloprid by three-dimensional fluorescence spectroscopy.Afterwards,six pre-processing algorithms and two dimensionality reduction algorithms were analyzed.Multiple scattering calibration(MSC)and uninformative variable elimination(UVE)were selected as the best pre-processing and wavelength selection methods,respectively.Finally,the broad learning system(BLS)was used for fluorescence spectroscopy modeling and compared with classical models such as partial least squares regression(PLSR),support vector machine(SVM),and deep extreme learning machines(DELM).The results showed that the BLS model obtained the best prediction of imidacloprid content.The test set coefficient of determination(R^(2)_(p))reached 0.949 and the root mean square error(RMSE)reached 0.347mg/kg.The research result showed that fluorescence spectroscopy combined with BLS was feasible to identify pesticide residue content,and it can provide a theoretical basis for the development of online detection system for pesticide residue content.
作者
刘翠玲
李佳琮
孙晓荣
殷莺倩
张善哲
吴静珠
LIU Cuiling;LI Jiacong;SUN Xiaorong;YIN Yingqian;ZHANG Shanzhe;WU Jingzhu(School of Artificial Intelligence,Beijing Technology and Business University,Beijing 100048,China;Beijing Key Laboratory of Big Data Technology for Food Safety,Bejing Technology and Business University,Beijing 100048,China)
出处
《农业机械学报》
EI
CAS
CSCD
北大核心
2023年第10期198-204,共7页
Transactions of the Chinese Society for Agricultural Machinery
基金
北京市自然科学基金项目(4222043)
国家自然科学基金项目(61807001)
北京工商大学2023研究生科研能力提升计划项目。
关键词
白菜
农药残留
三维荧光光谱
宽度学习系统
波段选择
cabbage
pesticide residue
three-dimensional fluorescence spectroscopy
broad learning system
wave selection
