Abstract
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 0.979 7, 0.997 2 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.
Publication Date
7-28-2020
First Page
55
Last Page
58
DOI
10.13652/j.issn.1003-5788.2020.07.011
Recommended Citation
Jing-jing, BAN; Gui-shan, LIU; Jian-guo, HE; Li-juan, CHENG; Nai-yun, FAN; and Rui-rui, YUAN
(2020)
"Detection of Enrofloxacin residues in chicken based on surface enhanced Raman spectroscopy and two-dimensional correlation spectroscopy,"
Food and Machinery: Vol. 36:
Iss.
7, Article 11.
DOI: 10.13652/j.issn.1003-5788.2020.07.011
Available at:
https://www.ifoodmm.cn/journal/vol36/iss7/11
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