摘要
利用表面增强拉曼光谱(SERS)技术结合二维相关光谱法(2D-COS)对鸡肉中恩诺沙星的拉曼光谱进行特征变量优选,使用偏最小二乘回归法(PLSR)建立恩诺沙星特征峰分析模型,并与竞争性正自适应加权算法(CARS)进行比较。结果表明,2D-COS-PLSR模型效果最优,其Rc、Rp分别为0.9797,0.9972,说明采用2D-COS优选鸡肉中恩诺沙星浓度相关的特征谱峰是可行的。
The surface-enhanced Raman spectroscopy(SERS)technology and the two-dimensional correlation spectroscopy(2D-COS)were used to optimize the characteristic variables of the Enrofloxacin in chicken meat.The partial least squares regression method(PLSR)was used to establish Enro the characteristic peak analysis model of sand star was compared with the competitive positive adaptive weighting algorithm(CARS).The results showed that the 2D-COS-PLSR model has the best effect,and its Rc and Rp were 0.9797,0.9972 respectively,which shows that it is feasible to use 2D-COS to optimize the characteristic spectral peaks related to the concentration of enrofloxacin in chicken.
作者
班晶晶
刘贵珊
何建国
程丽娟
樊奈昀
袁瑞瑞
BAN Jing-jing;LIU Gui-shan;HE Jian-guo;CHENG Li-juan;FAN Nai-yun;YUAN Rui-rui(School of Agriculture,Ningxia University,Yinchuan,Ningxia 750021,China)
出处
《食品与机械》
北大核心
2020年第7期55-58,共4页
Food and Machinery
基金
国家自然科学基金项目(编号:31560481,75002108A1651)。
关键词
表面增强拉曼光谱
恩诺沙星
二维相关光谱
特征峰
鸡肉
surface enhanced Raman spectroscopy
Enrofloxacin
two dimensional correlation spectrum
characteristic peak
chicken
