摘要
为提高大米中农药残留的表面增强拉曼光谱(SERS)快速检测精度,提出采用二维相关光谱(2DCOS)对大米拉曼光谱进行农药特征变量优选。首先,采用标准正态变量变换(SNV)对原始光谱预处理,再以甲基毒死蜱浓度为外扰,进行二维相关同步光谱和自相关谱解析,筛选出与甲基毒死蜱浓度变化最相关的特征谱峰,建立了大米中甲基毒死蜱残留浓度的支持向量机(SVM)分析模型,并与偏最小二乘(PLS)模型进行性能比较。结果表明,2DCOS方法能很好地筛选出与甲基毒死蜱浓度相关的特征谱峰;利用2DCOS优选出的4个甲基毒死蜱特征谱峰所建立的SVM模型性能优于PLS的实验结果,模型对预测集样本相关系数(RP)为0.96,均方根误差(RMSEP)为5.21,相对分析误差(RPD)为3.66,可用于大米中甲基毒死蜱农药残留的实际估测。研究表明,采用2DCOS优选大米中甲基毒死蜱浓度相关的特征变量是可行的,且能简化模型,提高模型预测精度,从而为拉曼光谱用于食品农产品质量安全的快速检测提供了一种新思路。
A two-dimensional correlation spectroscopy (2DCOS ) was presented to optimize the characteristic variables for pesticide residues in rice,in order to improve the accuracy for the rapid detection of pesticide residues in rice based on surface-enhanced Raman spectroscopy (SERS ).Firstly,the original spectra were pretreated using standard normal variable transformation (SNV ),then the two-dimensional correlation spectrum and diagnosis spectrum were analyzed with chlorpyrifos-methyl concentration as the disturbance.The characteristic peaks of chlorpyrifos-methyl were optimized based on the two-dimensional correlation spectroscopy and diagnosis spectroscopy.A support vector machine (SVM ) model for analyzing chlorpyrifos-methyl residues in rice was developed,and was compared with the PLS model.Results showed that2DCOS was a wonderful way for screening out the characteristic peaks related to the chlorpyrifos-methyl.The performance of SVM model based on 4 chlorpyrifos-methyl characteristic peaks selected by2DCOS was better than that of the PLS model.The correlation coefficient ( R p ) in the prediction set was 0.96,the root mean square error of prediction ( RMSEP ) was 5.21,and the relative prediction deviation ( RPD ) was3.66,which indicated that the developed model could be used for the actual estimation of chlorpyrifos-methyl pesticide residues in rice.Results showed that2DCOS is feasible for screening characteristic peaks related to chlorpyrifos-methyl in rice by simplifying the model and improve the prediction accuracy.It provides a new idea for the rapid detection of food and agricultural products by Raman spectroscopy for quality and safety.
作者
胡潇
黄俊仕
朱晓宇
刘鹏
吴瑞梅
邱霞
艾施荣
HU Xiao;HUANG Jun-shi;ZHU Xiao-yu;LIU Peng;WU Rui-mei;QIU Xia;AI Shi-rong(College of Computer and Information Engineering,Jiangxi Agricultural University,Nanchang 330045,China;College of Engineering,Jiangxi Agricultural University,Nanchang 330045,China;College of Food Science and Engineering,Jiangxi Agricultural University,Nanchang 330045,China)
出处
《分析测试学报》
CAS
CSCD
北大核心
2019年第8期946-952,共7页
Journal of Instrumental Analysis
基金
国家自然科学基金资助项目(31460315)
江西省对外科技合作计划(20151BDH80065)
关键词
表面增强拉曼光谱(SERS)
二维相关光谱(2DCOS)
特征变量优选
快速检测
大米
甲基毒死蜱
surface-enhanced Raman spectroscopy (SERS )
two-dimensional correlation spectroscopy (2DCOS )
characteristic variable optimization
rapid detection
rice
chlorpyrifos-methyl
